{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TorchSDE + Neuromancer: Moving from Neural ODEs to Neural SDEs and Motivating the Latent SDE Architecture\n",
    "\n",
    "This notebook goes over how to utilize torchsde's functionality within Neuromancer framework. This notebook is based off: https://github.com/google-research/torchsde/blob/master/examples/latent_sde_lorenz.py. In this example, we generate data according to a 3-dimensional stochastic Lorenz attractor. We then perform a \"system identification\" on this data -- seek to model a stochastic differential equation on this data. Upon performant training, this LatentSDE will then be able to reproduce samples that exhibit the same behavior as the provided Lorenz system. We train and utilize the Lightning framework to support custom functionality within the training loop.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### From Neural ODEs to Neural SDEs\n",
    "\n",
    "#### 1. Neural ODEs\n",
    "\n",
    "The general form of an ordinary differential equation (ODE) is:\n",
    "\n",
    "$$ \\frac{{dx}}{{dt}} = f(t, x) $$\n",
    "\n",
    "Neural ODEs parameterize the evolution of the system, in continuous-time, in terms of a neural network: $ \\frac{{dx}}{{dt}}  = f_{\\theta}(x_k)$ where $\\theta$ are network weights\n",
    "\n",
    "\n",
    "Next we need to solve the continuous-time NODE model with suitable ODE solver, e.g., [Runge–Kutta integrator](https://en.wikipedia.org/wiki/Runge%E2%80%93Kutta_methods).  \n",
    "\n",
    "$$ x_{k+1} = \\text{ODESolve}(f_{\\theta}(x_k)) $$\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "For training we need to obtain accurate reverse-mode gradients of the integrated NODE system. This can be done in two ways, either by unrolling the operations of the ODE solver and using the [backpropagation through time](https://en.wikipedia.org/wiki/Backpropagation_through_time) (BPTT) algorithm, or via [Adjoint state method](https://en.wikipedia.org/wiki/Adjoint_state_method). Assuming we have a suitable ODE solver (integrator) then we can train $f_{\\theta}(x_k)$ using our standard reference tracking loss (MSE) between predicted and ground-truth trajectory as they unroll.\n",
    "\n",
    "\n",
    "Neuromancer provides a set of ODE solvers implemented in [integrators.py](https://github.com/pnnl/neuromancer/blob/master/src/neuromancer/dynamics/integrators.py).\n",
    "For adjoint method we provide the interface to the [open-source implementation](https://github.com/rtqichen/torchdiffeq) via DiffEqIntegrator class.\n",
    "\n",
    "#### 2. Latent ODEs\n",
    "\n",
    "We can reformulate the above reference-tracking method for training Neural ODEs via a generative / variational inference approach. That is \n",
    "$$ z_{t_0} ∼ p(z_{t_0}) $$\n",
    "\n",
    "$$ z_{t_1}, z_{t_2}, ..., z_{t_N} = ODESolve(z_{t_0}, f, θ_f, t_0, ..., t_N) $$\n",
    "\n",
    "where each $$ x_{t_i} ∼ p(x|z_{t_i}, θ_x) $$\n",
    "\n",
    "In this way the evolution of the latent states follow an ordinary differential equation and we observe observations emitted from the latent system dynamics. Since we never actually observe the \"point estimates\" of $z$ we have to integrate over $z$ to learn p(x), which can be done by treating this as a standard variational autoencoder network and training thusly.\n",
    "\n",
    "#### 3. Neural ODEs with Added Stochasticicity\n",
    "\n",
    "Stochastic differential equations are an extension of ordinary differential equations in that they are ODEs with instantaneous noise dynamics added to their determinisitic system dynamics. We denote the former as the diffusion process; the latter the drift process. They are often used to model phenomena governed by many small and unobserved interactions in chemistry and microbiology such as molecular motion in liquid; allele frequencies in genetics. They are also known to model price fluctations in financial markets for short-medium time horizons. \n",
    "\n",
    "The general form of a stochastic differential equation is, in differential form:\n",
    "\n",
    "$$ dx = f(t, x) \\, dt + g(t, x) \\, dW $$\n",
    "\n",
    "Where $W$ denotes a Weiner process (e.g. Brownian motion). We can see that if $g()$ is zero then the SDE simplifies to an ODE. Thus for training SDEs, we need an equivalent BPTT or adjoint state method to compute tractable and scalable gradients. Li, et al recently developed such an analogous backpropagation algorithm in https://arxiv.org/pdf/2001.01328. In our codebase we leverage such SDE solvers via the torchSDE library (https://github.com/google-research/torchsde/tree/master/torchsde). For reference, the main reason we can use numerical solvers for this stochastic calculus case is that the above differential equation's dynamics can be interepreted as: \n",
    "\n",
    "$$ X_{t+\\Delta t} \\approx \\text{ODESolve}\\left(X_t, f(\\cdot) + g(\\cdot) \\frac{\\Delta W}{\\Delta t}, [t, t+\\Delta t]\\right) $$\n",
    "\n",
    "(See the Euler-Murayama method)\n",
    "\n",
    "\n",
    " Given that these solvers exist, we now move on to the question of **training these non-deterministic dynamics. What loss do we optimize**? \n",
    "\n",
    "Fitting an SDE in the same vein as an ODE (e.g. via maximal likelihood -- our reference tracking (MSE) loss since we assume Gaussian distribution) **will not work**: it will overfit and send the diffusion term to zero. Thus the inclusion of the diffusion term into our differential equation is a roadblock formulating the SDE in a rollout-based manner as we do with Neural ODEs. Devising loss functions to support rollout and physical constraints on the system is an open research question. \n",
    "\n",
    "#### 4. Latent SDEs\n",
    "\n",
    "It is natural to view training a Neural SDE from a probabilistic perspective: given training samples from a stochastic process, we wish to devise neural network(s) parameterizing the drift and diffusion terms (and perhaps other terms as well as shown below) that has the ability generate new data samples governed by the (learned) stochastic process. Thus we can employ the variational autoencoder approach similar to latent ODEs, but now the evolution of the latent states is modeled by an SDE and not an ODE. Other approaches exist as well, such as treating the SDE as Generative Adversarial Network (GAN). We only show the variational autoencoder form here.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "______"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 0. Imports\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<torch._C.Generator at 0x7f81683400b0>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import torch\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.gridspec as gridspec\n",
    "from torch import nn, optim\n",
    "from torch.utils.data import DataLoader\n",
    "from torch.distributions import Normal\n",
    "\n",
    "import tqdm\n",
    "import torchsde\n",
    "\n",
    "from neuromancer.psl import plot\n",
    "from neuromancer import psl\n",
    "from neuromancer.system import Node, System\n",
    "from neuromancer.dynamics import integrators, ode\n",
    "from neuromancer.trainer import Trainer, LitTrainer\n",
    "from neuromancer.problem import Problem\n",
    "from neuromancer.loggers import BasicLogger\n",
    "from neuromancer.dataset import DictDataset\n",
    "from neuromancer.constraint import variable\n",
    "from neuromancer.loss import PenaltyLoss\n",
    "from neuromancer.modules import blocks\n",
    "\n",
    "from typing import Sequence\n",
    "\n",
    "torch.manual_seed(0)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Neural ODE\n",
    "\n",
    "To motivate this walkthrough, we first construct a Neural ODE to solve a (deterministic) Lotka-Volterra System"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 500x100 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x1600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "def get_data(sys, nsim, nsteps, ts, bs):\n",
    "    \"\"\"\n",
    "    :param nsteps: (int) Number of timesteps for each batch of training data\n",
    "    :param sys: (psl.system)\n",
    "    :param ts: (float) step size\n",
    "    :param bs: (int) batch size\n",
    "\n",
    "    \"\"\"\n",
    "    train_sim, dev_sim, test_sim = [sys.simulate(nsim=nsim, ts=ts) for i in range(3)]\n",
    "    nx = sys.nx\n",
    "    nbatch = nsim//nsteps #500\n",
    "    length = (nsim//nsteps) * nsteps #1000\n",
    "    ts = torch.linspace(0,1,nsteps)\n",
    "    print('train sim ', train_sim['X'].shape)\n",
    "\n",
    "    trainX = train_sim['X'][:length].reshape(nbatch, nsteps, nx)\n",
    "    trainX = torch.tensor(trainX, dtype=torch.float32)\n",
    "\n",
    "    print(trainX.shape)# N x nsteps x state_size \n",
    "\n",
    "    train_data = DictDataset({'X': trainX, 'xn': trainX[:, 0:1, :]}, name='train')\n",
    "    train_loader = DataLoader(train_data, batch_size=bs,\n",
    "                              collate_fn=train_data.collate_fn, shuffle=True)\n",
    "\n",
    "    devX = dev_sim['X'][:length].reshape(nbatch, nsteps, nx)\n",
    "    devX = torch.tensor(devX, dtype=torch.float32)\n",
    "    dev_data = DictDataset({'X': devX, 'xn': devX[:, 0:1, :]}, name='dev')\n",
    "    dev_loader = DataLoader(dev_data, batch_size=bs,\n",
    "                            collate_fn=dev_data.collate_fn, shuffle=True)\n",
    "\n",
    "    testX = test_sim['X'][:length].reshape(1, nsim, nx)\n",
    "    testX = torch.tensor(testX, dtype=torch.float32)\n",
    "    test_data = {'X': testX, 'xn': testX[:, 0:1, :]}\n",
    "\n",
    "    return train_loader, dev_loader, test_data, trainX\n",
    "\n",
    "torch.manual_seed(0)\n",
    "\n",
    "# %%  ground truth system\n",
    "system = psl.systems['LotkaVolterra']\n",
    "modelSystem = system()\n",
    "ts = modelSystem.ts\n",
    "nx = modelSystem.nx\n",
    "raw = modelSystem.simulate(nsim=1000, ts=ts)\n",
    "plt.figure(figsize=(5,1))\n",
    "plot.pltOL(Y=raw['X'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We construct the NODE for the Lotka-Volterra system, creating a class inheriting from the neuromancer.ode ODESystem base class with learnable parameters\n",
    "\n",
    "The standard Lotka-Volterra system, also known as the predator-prey model, describes the dynamics of two interacting species in a biological community. The system consists of two coupled ordinary differential equations (ODEs), typically represented as follows:\n",
    "\n",
    "$$\n",
    "\\begin{align*}\n",
    "\\frac{dX}{dt} &= (\\alpha X - \\beta XY) dt  \\\\\n",
    "\\frac{dY}{dt} &= (\\delta XY - \\gamma Y) dt\n",
    "\\end{align*}\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class LotkaVolterraHybrid(ode.ODESystem):\n",
    "\n",
    "    def __init__(self, block, insize=2, outsize=2):\n",
    "        super().__init__(insize=insize, outsize=outsize)\n",
    "\n",
    "        self.block = block\n",
    "        self.alpha = nn.Parameter(torch.tensor([.10]), requires_grad=True)\n",
    "        self.beta = nn.Parameter(torch.tensor([.10]), requires_grad=True)\n",
    "        self.delta = nn.Parameter(torch.tensor([.10]), requires_grad=True)\n",
    "        self.gamma = nn.Parameter(torch.tensor([.10]), requires_grad=True)\n",
    "        assert self.block.in_features == 2\n",
    "        assert self.block.out_features == 1\n",
    "\n",
    "    def ode_equations(self, x):\n",
    "        #Lotka Volterra equations\n",
    "        x1 = x[:, [0]]\n",
    "        x2 = x[:, [-1]]\n",
    "        dx1 = self.alpha*x1 - self.beta*self.block(x)\n",
    "        dx2 = self.delta*self.block(x) - self.gamma*x2\n",
    "        return torch.cat([dx1, dx2], dim=-1)\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create the Neuromancer System to train this model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train sim  (1000, 2)\n",
      "torch.Size([500, 2, 2])\n"
     ]
    }
   ],
   "source": [
    "nsim = 1000\n",
    "nsteps = 2\n",
    "bs = 10\n",
    "train_loader, dev_loader, test_data, trainX = \\\n",
    "    get_data(modelSystem, nsim, nsteps, ts, bs)\n",
    "\n",
    "# construct UDE model in Neuromancer\n",
    "net = blocks.MLP(2, 1, bias=True,\n",
    "                    linear_map=torch.nn.Linear,\n",
    "                    nonlin=torch.nn.GELU,\n",
    "                    hsizes=4*[20])\n",
    "fx = LotkaVolterraHybrid(net)\n",
    "\n",
    "# integrate UDE model\n",
    "fxRK4 = integrators.RK4(fx, h=ts)\n",
    "# create symbolic UDE model\n",
    "ude = Node(fxRK4, ['xn'], ['xn'], name='UDE')\n",
    "dynamics_model = System([ude])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can train the above model using standard Neuromancer training set-up with reference and finite-difference loss functions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# %% Constraints + losses:\n",
    "x = variable(\"X\")\n",
    "xhat = variable('xn')[:, :-1, :]\n",
    "# finite difference variables\n",
    "xFD = (x[:, 1:, :] - x[:, :-1, :])\n",
    "xhatFD = (xhat[:, 1:, :] - xhat[:, :-1, :])\n",
    "\n",
    "# trajectory tracking loss\n",
    "reference_loss = (xhat == x)^2\n",
    "reference_loss.name = \"ref_loss\"\n",
    "\n",
    "# finite difference loss\n",
    "fd_loss = 2.*(xFD == xhatFD)^2\n",
    "fd_loss.name = 'FD_loss'\n",
    "\n",
    "# aggregate list of objective terms and constraints\n",
    "objectives = [reference_loss, fd_loss]\n",
    "constraints = []\n",
    "# create constrained optimization loss\n",
    "loss = PenaltyLoss(objectives, constraints)\n",
    "# construct constrained optimization problem\n",
    "problem = Problem([dynamics_model], loss)\n",
    "\n",
    "optimizer = torch.optim.Adam(problem.parameters(), lr=0.001)\n",
    "logger = BasicLogger(args=None, savedir='test', verbosity=1,\n",
    "                     stdout=['dev_loss', 'train_loss'])\n",
    "# define neuromancer trainer\n",
    "trainer = Trainer(\n",
    "    problem,\n",
    "    train_loader,\n",
    "    dev_loader,\n",
    "    test_data,\n",
    "    optimizer,\n",
    "    patience=50,\n",
    "    warmup=100,\n",
    "    epochs=500,\n",
    "    eval_metric=\"dev_loss\",\n",
    "    train_metric=\"train_loss\",\n",
    "    dev_metric=\"dev_loss\",\n",
    "    test_metric=\"dev_loss\",\n",
    "    logger=logger,\n",
    ")\n",
    "trainer.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Neural ODEs with Added Stochasticity\n",
    "\n",
    "We now attempt to add stochasticiity to the Neural ODE case and train likewise. We will show that this model **cannot** be trained using our standard Neural ODE training setup and thus it will necessitate a different approach -- the Latent SDE shown in section 3. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The stochastic Lotka-Volterra system is given by:\n",
    "\n",
    "$$\n",
    "\\begin{align*}\n",
    "\\frac{dX}{dt} &= (\\alpha X - \\beta XY) dt + \\sigma_1 dW_1 \\\\\n",
    "\\frac{dY}{dt} &= (\\delta XY - \\gamma Y) dt + \\sigma_2 dW_2\n",
    "\\end{align*}\n",
    "$$\n",
    "\n",
    "where the additional terms are the diffusion coefficients.\n",
    "How can we represent this in NeuroMANCER?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SDE Base Class\n",
    "\n",
    "We define a base class for SDEs here (as well as in sde.py). Note that the noise_type and sde_type are hardcoded to ensure correct functionality with torchSDE. Note that the two main functions required to be implemenented are the $f$ and $g$ functions as discussed earlier. The $f$ function is equivalent to the ODESystem's `ode_equations()` but with a different signature to be compatible with torchSDE. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import abc\n",
    "class BaseSDESystem(abc.ABC, nn.Module):\n",
    "    \"\"\"\n",
    "    Base class for SDEs for integration with TorchSDE library\n",
    "    \"\"\"\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.noise_type = \"diagonal\" #only supports diagonal diffusion right now\n",
    "        self.sde_type = \"ito\" #only supports Ito integrals right now\n",
    "        self.in_features = 0 #for compatibility with Neuromancer integrators; unused\n",
    "        self.out_features = 0\n",
    "\n",
    "    @abc.abstractmethod\n",
    "    def f(self, t, y):\n",
    "        \"\"\"\n",
    "        Define the ordinary differential equations (ODEs) for the system.\n",
    "\n",
    "        Args:\n",
    "            t (Tensor): The current time (often unused)\n",
    "            y (Tensor): The current state variables of the system.\n",
    "\n",
    "        Returns:\n",
    "            Tensor: The derivatives of the state variables with respect to time.\n",
    "                    The output should be of shape [batch size x state size]\n",
    "        \"\"\"\n",
    "        pass\n",
    "\n",
    "    @abc.abstractmethod\n",
    "    def g(self, t,y):\n",
    "        \"\"\"\n",
    "        Define the diffusion equations for the system.\n",
    "\n",
    "        Args:\n",
    "            t (Tensor): The current time (often unused)\n",
    "            y (Tensor): The current state variables of the system.\n",
    "\n",
    "        Returns:\n",
    "            Tensor: The diffusion coefficients per batch item (output is of size \n",
    "                    [batch size x state size]) for noise_type 'diagonal'\n",
    "        \"\"\"\n",
    "        pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We implement the stochastic Lotka-Volterra system with dummy parameters using this base class. We initially set the diffusion coefficients to zero to show that this simplifies to an ODE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "class LotkaVolterraSDE(BaseSDESystem):\n",
    "    def __init__(self, a, b, c, d, g_params):\n",
    "        super().__init__()\n",
    "        self.a = a\n",
    "        self.b = b\n",
    "        self.c = c\n",
    "        self.d = d\n",
    "        self.g_params = g_params\n",
    "\n",
    "\n",
    "    def f(self, t, x):\n",
    "        x1 = x[:,[0]]\n",
    "        x2 = x[:,[1]]\n",
    "        dx1 = self.a * x1 - self.b * x1*x2\n",
    "        dx2 = self.c * x1*x2 - self.d * x2\n",
    "        return torch.cat([dx1, dx2], dim=-1)\n",
    "\n",
    "    def g(self, t, x):\n",
    "        return self.g_params\n",
    "\n",
    "# Define parameters\n",
    "a = 1.1    # Prey growth rate\n",
    "b = 0.4   # Predation rate\n",
    "c = 0.1   # Predator growth rate\n",
    "d = 0.4   # Predator death rate\n",
    "sigma1 = 0\n",
    "sigma2 = 0\n",
    "g_params = torch.tensor([[sigma1, sigma2]])\n",
    "# Create the SDE model\n",
    "sde = LotkaVolterraSDE(a, b, c, d, g_params)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's generate data from this ODE: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define time span\n",
    "import torchsde \n",
    "\n",
    "t_span = torch.linspace(0, 20, 2000)\n",
    "\n",
    "# Initial condition\n",
    "x0 = torch.tensor([10.0, 10.0]).unsqueeze(0) #[1x2]\n",
    "\n",
    "# Integrate the SDE model\n",
    "data = torchsde.sdeint(sde, x0, t_span, method='euler')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vX15iSJzLzp07efLJJ/n222/d7eni+l3k5uaW+r0kJycDZgGHRx55hIULF3LRRRcxdepUcnJy3B/sTvZcAFq1asWPP/5YqWIFmzZtYtWqVSdNfP5aROSvv8fT3ZeWlsaoUaOYNGlSqWOV9Roty5133snAgQMJCgoiPj7ePb/sdOfftGkTcHxOzl/FxsYCFX8tfv/994wePZoVK1aUmKtTnqGDhw4dIicn56S/R6fTya5du0q8xk58XgUFBaSlpZV4XGJiYokk6quvviI2NpaQkBDq1avnHuZ3orp165YquLFp0yb+/PPPcr0WbrnlFj7//HPmz59Ply5dmDVrFgcOHODmm28+TQuUdMMNN/DII48wffp0JkyYwBVXXEFMTEyp/SrSbjt27Cg1ZBFK/+2U9/UhIp6lJElETio0NJSOHTvSsWNHmjdvzq233srkyZN56qmnTvm4+++/n3HjxvHQQw/RqVMn4uLisNls3HDDDWVOUj7Z1WijEqWBn3vuOf79738zfPhwnnnmGWrWrElQUBAPPfRQmecuaz6ALxkwYACTJk3ihRde4OOPPy7RM1VUVESvXr1IS0vj//7v/2jZsiVRUVHs2bOHYcOGudvj888/L3X13tX2N954I4899hgTJ07koosuYuLEidSoUYN+/fp59Hk5nU569erFY489Vub9zZs3L/HzqX6PZd03aNAgFi5cyKOPPkrbtm2Jjo7G6XTSt2/fk06k/6tmzZrRs2fP0+731/O7jv/JJ5+4k9ETBQdX/F/z/Pnz6d+/P126dOGdd96hTp06hISEMG7cuDKLb1SFE5/XwoUL6d69e4n7t23bVmLuTJcuXdzV7cpzTBen08nZZ5/Nq6++WuZjUlNT3bf79OlD7dq1+fTTT+nSpQuffvopycnJ5fo9nahOnTp069aNV155hV9//bVaK9p54vUhIhWnvzQRKZfzzjsPMIcAuZzsCvWXX37J0KFDeeWVV9zb8vLySlRuq4gGDRoA5hXWE4e0HDp0qFTviKvq2gcffFBie3p6+mk/oP31XH+1YcOGCsd+qnNs2LChxPMpKChg27ZtJT7Qna4X4KqrrqJ3794MGzaMmJgYxo4d675v9erVbNy4kY8++ohbbrnFvX3mzJkljtGnT59S21xSUlLo3r07kydP5t///jczZ85k2LBh7qv9Jz6Xv1q/fj0JCQmn7EU62fNr0qQJWVlZFf5wWx5Hjx5l9uzZjBo1iieffNK9vazfuSe4elCSkpJO+fwq8lr86quvCA8P58cffyzRmzVu3LhSjy2rzRMTE4mMjDzp7zEoKKhEMvJX5557bqnXUFkf8CujSZMmrFy5kh49epz278FutzN48GDGjx/Piy++yNSpUys8LNBl8ODB3H777cTHx5/0okBF2q1Bgwbl+l2W9/UhIp6lOUkiUsKcOXPK7MFxzS05cWhIVFRUmYmP3W4vdYw333yzxNC3iujZsychISG8+eabJY5b1iKrZZ178uTJ7Nmzp1znqlOnDm3btuWjjz4qMexq5syZrFu3rlLx/1XPnj0JDQ3ljTfeKBHrBx98QEZGhrsSGZhtfLrhX7fccgtvvPEG7777Lv/3f//n3u76YHjiOQzDKLUocJ06dejZs2eJrxMNGTKEgwcP8re//Q2Hw1FiDsWJ7XXia2HNmjX89NNPp+1xOtlraNCgQfz222/8+OOPpe5LT0+nsLDwlMc9lbLaBcp+PXlCnz59iI2N5bnnnsPhcJS631X+viKvRbvdjs1mK/E3tn379jIXjS2rze12O7179+abb74pUar/wIEDTJw4kYsvvviUw7xq1KhR6jVUVQuvDho0iD179vC///2v1H25ubmlKuvdfPPNHD16lL/97W9kZWVVesHj6667jqeeeop33nmnzDW3oGLt1q9fPxYtWlRiodpDhw4xYcKEEscs7+tDRDxLPUkiUsL9999PTk4OV199NS1btqSgoICFCxfy+eef07BhwxLDsjp06MCsWbN49dVXSUlJoVGjRlxwwQVcccUVfPLJJ8TFxXHWWWfx22+/MWvWrDLnBJVHYmIijzzyCM8//zxXXHEF/fr1Y/ny5UyfPr1U79AVV1zB008/za233spFF13E6tWrmTBhQokem9N5/vnnufzyy7n44osZPnw4aWlpvPnmm7Ru3ZqsrKxyHePQoUOMHj261PZGjRoxZMgQHn/8cUaNGkXfvn3p378/GzZs4J133qFjx44lPtR16NCBzz//nBEjRtCxY0eio6O58sorSx33vvvuIzMzk3/961/ExcXxz3/+k5YtW9KkSRMeeeQR9uzZQ2xsLF999VWp3rfTufbaa7nnnnv45ptvSE1NpUuXLiXu/89//sNll11Gp06duO2229wlwOPi4hg5cuQpj92hQwfGjh3L6NGjadq0KUlJSVx66aU8+uij7gIRw4YNo0OHDmRnZ7N69Wq+/PJLtm/fXq6ewbLExsbSpUsXXnrpJRwOB3Xr1uWnn35yr9fjabGxsYwdO5abb76Z9u3bc8MNN5CYmMjOnTuZNm0anTt35q233gLK/1q8/PLLefXVV+nbty+DBw/m4MGDvP322zRt2pRVq1aVOP/J/m5Hjx7NzJkzufjii7nnnnsIDg7mvffeIz8/n5deeqla2qYsN998M1988QV33XUXc+bMoXPnzhQVFbF+/Xq++OILfvzxR3dPN0C7du1o06aNu+BD+/btK3Xe8rx+gXK322OPPcYnn3xC3759efDBB90lwBs0aFDid1SR14eIeJAlNfVExGtNnz7dGD58uNGyZUsjOjraCA0NNZo2bWrcf//9xoEDB0rsu379eqNLly5GREREiVLcR48eNW699VYjISHBiI6ONvr06WOsX7/eaNCgQYnSw65Swn/88UeJ47rK7M6ZM8e9raioyBg1apRRp04dIyIiwujWrZuxZs2aUsfMy8sz/v73v7v369y5s/Hbb78ZXbt2LVFquqxSvif66quvjFatWhlhYWHGWWedZXz99dfG0KFDy10CHCjzq0ePHu793nrrLaNly5ZGSEiIUbt2bePuu+82jh49WuJYWVlZxuDBg434+PgSZZ9PFv9jjz1mAMZbb71lGIZhrFu3zujZs6cRHR1tJCQkGHfccYexcuVKAzDGjRt32ufiMnDgQAMwHnvssTLvnzVrltG5c2cjIiLCiI2NNa688kpj3bp1JfYpqwT4/v37jcsvv9yIiYkxgBK/o2PHjhmPP/640bRpUyM0NNRISEgwLrroIuPll192l6R3leP+z3/+UyomV3nkQ4cOlbpv9+7dxtVXX23Ex8cbcXFxxsCBA429e/cagPHUU0+dsi1Odc6ynu9fX98uc+bMMfr06WPExcUZ4eHhRpMmTYxhw4YZS5YsKbFfeV+LH3zwgdGsWTMjLCzMaNmypTFu3LgyS0Sf7O/WMAxj2bJlRp8+fYzo6GgjMjLS6N69u7Fw4cIKPa+/OtXv4USnKp1fUFBgvPjii0br1q2NsLAwo0aNGkaHDh2MUaNGuUvRn+ill14yAOO5554rV4ynO7/Lyf7uytNuhmEYq1atMrp27WqEh4cbdevWNZ555hnjgw8+KPV34TrX6V4fKgEu4jk2w6jEzGgRERERL/X666/z8MMPs3379lLVM0VEykNJkoiIiPgNwzA499xzqVWrltYUEpFK05wkERER8XmuhY7nzJnD6tWr+eabb6wOSUR8mHqSRERExOdt376dRo0aER8fzz333MOzzz5rdUgi4sOUJImIiIiIiJxA6ySJiIiIiIicQEmSiIiIiIjICfy+cIPT6WTv3r3ExMRgs9msDkdERERERCxiGAbHjh0jJSWFoKCT9xf5fZK0d+9eUlNTrQ5DRERERES8xK5du6hXr95J7/f7JCkmJgYwGyI2NtbSWBwOBz/99BO9e/cmJCTE0lj8kdrXs9S+nqX29Sy1r2epfT1PbexZal/P8qb2zczMJDU11Z0jnIzfJ0muIXaxsbFekSRFRkYSGxtr+QvEH6l9PUvt61lqX89S+3qW2tfz1Maepfb1LG9s39NNw1HhBhERERERkRMoSRIRERERETmBpUnS2LFjOeecc9xD4Tp16sT06dPd9+fl5XHvvfdSq1YtoqOjufbaazlw4ICFEYuIiIiIiL+zdE5SvXr1eOGFF2jWrBmGYfDRRx8xYMAAli9fTuvWrXn44YeZNm0akydPJi4ujvvuu49rrrmGX3/91cqwRURERKQaGYZBYWEhRUVFHjm+w+EgODiYvLw8j50jkFVn+9rtdoKDg8946R9Lk6Qrr7yyxM/PPvssY8eOZdGiRdSrV48PPviAiRMncumllwIwbtw4WrVqxaJFi7jwwgvLPGZ+fj75+fnunzMzMwHzl+NwODz0TMrHdX6r4/BXal/PUvt6ltrXs9S+nqX29bxAbmOHw8GBAwfIzc312DkMwyA5OZmdO3dqXU0PqO72jYiIoHbt2mUWiSjv35DNMAyjqgOrjKKiIiZPnszQoUNZvnw5+/fvp0ePHhw9epT4+Hj3fg0aNOChhx7i4YcfLvM4I0eOZNSoUaW2T5w4kcjISE+FLyIiIiIeULt2baKjo6lZsybBwX5fmFnOUGFhIWlpaWRlZZU5TScnJ4fBgweTkZFxysrXlr/SVq9eTadOncjLyyM6OpopU6Zw1llnsWLFCkJDQ0skSGD+oezfv/+kx3v88ccZMWKE+2dXLfTevXt7RQnwmTNn0qtXL68pf+hP1L6epfb1LLWvZ6l9PUvt63mB2sb5+fns3LmT+vXre/Rit2EYHDt2jJiYGPUkeUB1t29sbCw7d+6kTZs2hIWFlbjPNcrsdCxPklq0aMGKFSvIyMjgyy+/ZOjQofzyyy+VPl5YWFipxgAICQnxmjcVb4rFH6l9PUvt61lqX89S+3qW2tfzAq2Ni4qKsNlsBAcHExTkuXpjTqcTMNfO8eR5AlV1t69rTlJwcHCpv5fy/v1YniSFhobStGlTADp06MAff/zB66+/zvXXX09BQQHp6eklepMOHDhAcnKyRdGKiIiIiIi/87pU2el0kp+fT4cOHQgJCWH27Nnu+zZs2MDOnTvp1KmThRGKiIiIiIg/szRJevzxx5k3bx7bt29n9erVPP7448ydO5chQ4YQFxfHbbfdxogRI5gzZw5Lly7l1ltvpVOnTietbCciIiIiIqXNnj2bVq1aVWkJ7hkzZtC2bVv3cDp/YmmSdPDgQW655RZatGhBjx49+OOPP/jxxx/p1asXAK+99hpXXHEF1157LV26dCE5OZmvv/7aypBFRERERE5r2LBh2Gw2bDabe3rJ008/TWFhoSXxPPbYYzzxxBPY7fZyP+aBBx6gQ4cOhIWF0bZt21L39+3bl5CQECZMmFCFkXoHS+ckffDBB6e8Pzw8nLfffpu33367miISEREREakaffv2Zdy4ceTn5/PDDz9w7733EhISwuOPP15q34KCAkJDQz0Sx4IFC9iyZQvXXntthR87fPhwFi9ezKpVq8q8f9iwYbzxxhvcfPPNZxqmV/G6OUkiIiIiIidjGAY5BYVV/pVbUHTafSq6vGhYWBjJyck0aNCAu+++m549e/Ltt98CZnJx1VVX8eyzz5KSkkKLFi0A2LVrF4MGDSI+Pp6aNWsyYMAAtm/fDsC8efMICQkptRzOQw89xCWXXHLSOCZNmkSvXr0IDw93t2HPnj3p06eP+zmlpaVRr149nnzySffj3njjDe69914aN2580mNfeeWVLFmyhC1btlSobbyd5dXtRERERETKK9dRxFlP/mjJudc93YfI0Mp/fI6IiODIkSPun2fPnk1sbCwzZ84EzPWw+vTpQ6dOnZg/fz7BwcGMHj2avn37smrVKrp06ULjxo355JNPePTRR92PmTBhAi+99NJJzzt//nwGDx7s/tlms/HRRx9x9tln88Ybb/Dggw9y1113Ubdu3RJJUnnUr1+f2rVrM3/+fJo0aVKhx3ozJUkiIiIiIh5kGAazZ8/mxx9/5P7773dvj4qK4v3333cPs/v0009xOp28//777kVXx40bR3x8PHPnzqV3797cdtttjBs3zp0kfffdd+Tl5TFo0KCTnn/Hjh2kpKSU2Fa3bl3ee+89brnlFvbv388PP/zA8uXLCQ6ueHqQkpLCjh07Kvw4b6YkSURERPyWYRis2p1BQZGTDvVrEBRkszokOUMRIXbWPd2nSo/pdDo5lnmMmNiYUy52GhFS/qIHAN9//z3R0dE4HA6cTieDBw9m5MiR7vvPPvvsEvOQVq5cyebNm4mJiSlxnLy8PPdwtmHDhvHEE0+waNEiLrzwQsaPH8+gQYOIioo6aRy5ubnuoXYnGjhwIFOmTOGFF15g7NixNGvWrELPzyUiIoKcnJxKPdZbKUkSERERv5TnKOLeCcuYvf4gAD1aJvH2kPaEV/CDrngXm812RkPeyuJ0OikMtRMZGnzKJKmiunfvztixYwkNDSUlJaVUL81fE5usrCw6dOhQZrW4xMREAJKSkrjyyisZN24cjRo1Yvr06cydO/eUcSQkJHD06NFS23Nycli6dCl2u51NmzZV8Nkdl5aW5o7PXyhJEhEREb/0wvT1zF5/EFfn0ez1B3nz50082qeltYFJwIiKiqJp06bl3r99+/Z8/vnnJCUlERsbe9L9br/9dm688Ubq1atHkyZN6Ny58ymP265dO9atW1dq+9///neCgoKYPn06/fr14/LLL+fSSy8td7xwvJerXbt2FXqct1N1OxEREfE7Ww9l8dFv2wH4YFhH3h7cHoD/ztvKwWN5FkYmcnJDhgwhISGBAQMGMH/+fLZt28bcuXN54IEH2L17t3u/Pn36EBsby+jRo7n11ltPe9w+ffqwYMGCEtumTZvGhx9+yIQJE+jVqxePPvooQ4cOLdHjtHnzZlasWMH+/fvJzc1lxYoVrFixgoKCAvc+ixYtIiwsjE6dOlVBC3gPJUkiIiLid979ZQuGAT1bJdG9RRKXnV2H9vXjcRQZTFi00+rwRMoUGRnJvHnzqF+/Ptdccw2tWrXitttuIy8vr0TPUlBQEMOGDaOoqIhbbrnltMcdMmQIa9euZcOGDQAcOnSI2267jZEjR9K+vXkBYdSoUdSuXZu77rrL/bjbb7+ddu3a8d5777Fx40batWtHu3bt2Lt3r3ufzz77jCFDhhAZGVlVzeAVNNxORERE/EpadgFTlu8B4O5ux4c6Db2oIct2ruCbFXt4qGczd/UwEU8YP358pe5PTk7mo48+Ou3x9+zZQ79+/ahTp85p961Zsyb33Xcfr776Ku+99x6JiYml1loKCQlhyZIlJbadbq7T4cOH+fLLL0s9zh+oJ0lERET8yvQ1+3AUGZxVJ5YODWq4t/dsVZuw4CC2H8nhz33HLIxQpPIyMjJYsGABEydOLFFO/HT+9a9/0aBBA5xOZ5XFsn37dt555x0aNWpUZcf0FkqSRERExK98u8IcCjSgbcl1YaLCgrmkmVmBa86Gg9Uel0hVGDBgAL179+auu+6iV69e5X5cfHw8//znP6u0et95553H9ddfX2XH8yYabiciIiJ+4+CxPH7fngbAFeemlLr/kmYJzPrzAAu3HObe7uWvOibiLU43BE6qhnqSRERExG/M23gYw4Cz68ZRNz6i1P2dm9YC4I/tR8lzFFV3eCLiI5QkiYiIiN+Yt/EQAN1alL2wZZPEaGpFhVJQ6OTPfZnVGZqI+BAlSSIiIuIXipwG8zeZSVKX5mUnSTabjbPrxQGwandGtcUmIr5FSZKIiIj4hT/3ZXI0x0FMWDBtU+NPut859cz7lCSJyMkoSRIRERG/8EdxwYbzGtYgxH7yjzjnunuS0qsjLBHxQUqSRERExC8s2X4UgPMa1jzlfq7hdpsPZZGVX+jxuETE9yhJEhEREZ9nGMbxnqQTFpAtS1JMOEkxYRgGbDygRWXF9w0bNoyrrrrK6jBKmT17Nq1ataKoqOoqSb777rtceeWVVXa8k1GSJCIiIj5vV1ouB4/lE2K3ce4p5iO5NK8dA8DmA1kejkwC1bBhw7DZbNhsNkJDQ2natClPP/00hYXW915u374dm83GihUrPHqexx57jCeeeAK73V6u/VeuXMmNN95IamoqERERtGrVitdff73EPsOHD2fZsmXMnz/fEyG7KUkSERERn+fqRTq7bhzhIaf/QNY0KRqATQfVkySe07dvX/bt28emTZv4+9//zsiRI/nPf/5T5r4FBQXVHF3VcDgcZW5fsGABW7Zs4dprry33sZYuXUpSUhKffvopa9eu5V//+hePP/44b731lnuf0NBQBg8ezBtvvHHGsZ+KkiQRERHxeUt3lm8+kkuz2q4kST1JPscwoCC76r8cOaffxzAqFGpYWBjJyck0aNCAu+++m549e/Ltt98Cx4fIPfvss6SkpNCiRQsAdu3axaBBg4iPj6dmzZoMGDCA7du3u49ZVFTEiBEjiI+Pp1atWjz22GMYf4lrxowZXHzxxe59rrjiCrZs2eK+v1GjRgC0a9cOm81Gt27dAHA6nTz99NPUq1ePsLAw2rZty4wZM9yPc/VAff7553Tt2pXw8HAmTJhQ5nOfNGkSvXr1Ijw8vPjXZtC7d2/69OnjjjctLY169erx5JNPAmYv0euvv07Xrl1p3LgxN910E7feeitff/11iWNfeeWVfPvtt+Tm5lbo91ERwR47soiIiEg1WV1czvuc4qIMp9MsyRxut0nD7XyPIweeS6nSQwYB8eXZ8Z97ITSq0ueJiIjgyJEj7p9nz55NbGwsM2fOBMxemT59+tCpUyfmz59PcHAwo0ePpm/fvqxatYrQ0FBeeeUVxo8fz4cffkirVq145ZVXmDJlCpdeeqn7uNnZ2YwYMYJzzjmHrKwsnnzySa6++mpWrFhBUFAQv//+O+effz6zZs2idevWhIaGAvD666/zyiuv8N5779GuXTs+/PBD+vfvz9q1a2nWrJn7+P/4xz945ZVXaNeunTsJ+qv58+czePBg9882m41x48Zx7rnn8sYbb/Dggw9y1113UbduXXeSVJaMjAxq1ix58eO8886jsLCQxYsXuxO8qqYkSURERHxaQaGTDfvNYXNn1y1vkmT2JO1JzyU7v5CoMH0kEs8xDIPZs2fz448/cv/997u3R0VF8f7777uTlE8//RSn08n777+PzWYDYNy4ccTHxzN37lx69+7NmDFjePzxx7nmmmsAs5DBjz/+WOJ8fx3i9uGHH5KYmMi6deto06YNiYnmYsu1atUiOTnZvd/LL7/M//3f/3HDDTcA8OKLLzJnzhzGjBnD22+/7d7voYcecp//ZHbs2EFKSslktm7durz33nvccsst7N+/nx9++IHly5cTHFz239/ChQv5/PPPmTZtWontkZGRxMXFsWPHjlPGcCb0jiAiIiI+beOBYxQUOYkND6Z+zchyPaZGVCi1okI5kl3A1kPZ7rLg4gNCIs0enSrkdDrJPHaM2JgYgoJOMRslpHyvL5fvv/+e6OhoHA4HTqeTwYMHM3LkSPf9Z599tjtBArNwwebNm4mJiSlxnLy8PLZs2UJGRgb79u3jggsucN8XHBzMeeedV2LI3aZNm3jyySdZvHgxhw8fxul0ArBz507atGlTZqyZmZns3buXzp07l9jeuXNnVq5cWWLbeeedd9rnnpubW2Yv08CBA5kyZQovvPACY8eOLdFDdaI1a9YwYMAAnnrqKXr37l3q/oiICHJyck4bR2UpSRIRERGftmaPOdSuTd0499X38mhQK5Ij2QXsSFOS5FNstjMa8lYmpxNCiszjnipJqqDu3bszduxYQkNDSUlJKdVjEhVV8nlkZWXRoUOHMuf5uHp/yuPKK6+kQYMG/O9//yMlJQWn00mbNm2qrDjEX+MuS0JCAkePHi21PScnh6VLl2K329m0aVOZj123bh09evTgzjvv5Iknnihzn7S0tAq1SUWpcIOIiIj4tNXFSVJ5h9q5NKhlftDbccRzV6MlsEVFRdG0aVPq169/0iFlJ2rfvj2bNm0iKSmJpk2blviKi4sjLi6OOnXqsHjxYvdjCgsLWbp0qfvnI0eOsGHDBp544gl69OhBq1atSiUrrt6rE9cvio2NJSUlhV9//bXEvr/++itnnXVWhZ97u3btWLduXantf//73wkKCmL69Om88cYb/PzzzyXuX7t2Ld27d2fo0KE8++yzZR57y5Yt5OXl0a5duwrHVV5KkkRERMSnrdmbCZg9SRXhGpq3U0mSeIkhQ4aQkJDAgAEDmD9/Ptu2bWPu3Lk88MAD7N69G4AHH3yQF154galTp7J+/Xruuece0tPT3ceoUaMGtWrV4r///S+bN2/m559/ZsSIESXOk5SUREREBDNmzODAgQNkZJgXGh599FFefPFFPv/8czZs2MA//vEPVqxYwYMPPljh59KnTx8WLFhQYtu0adP48MMPmTBhAr169eLRRx9l6NCh7iRuzZo1dO/end69ezNixAj279/P/v37OXToUInjzJ8/n8aNG9OkSZMKx1VeSpJERETEZzmKnPy5z0ySKt6TZCZJO9KyqzwukcqIjIxk3rx51K9fn2uuuYZWrVpx2223kZeXR2xsLGD2xNx8880MHTqUTp06ERMTw9VXX+0+RlBQEJMmTWLp0qW0adOGhx9+uNTaTMHBwbzxxhu89957pKSkMGDAAAAeeOABRowYwd///nfOPvtsZsyYwbfffnvSeUOnMmTIENauXcuGDRsAOHz4MHfccQcjR46kffv2AIwaNYratWtz1113AfDll19y6NAhPv30U+rUqeP+6tixY4ljf/bZZ9xxxx0VjqkiNCdJREREfNamA1kUFDqJCQ92Jz3l5dpfPUniCePHj6/U/cnJyXz00UcnfVxwcDBjxoxhzJgxJ92nZ8+epYa6/XUtpdtvv53bb7+9xLagoCCeeuopnnrqqTKP27Bhw1LHOZmaNWty33338eqrrzJ27FgSEhLYu3dvicIYISEhLFmyxP3zyJEjSxS2KMvatWtZsWIFX3zxRbniqCz1JImIiIjPWrvXHCbUOiW2QkUbAOrXNOck7cvMI7+w6DR7i0hF/etf/6JBgwbu6npVYd++fXz88cfExXm22Ip6kkRERMRnbTxgro/UMjm2wo9NiA4lMtROTkERu4/m0iQxuqrDEwlo8fHx/POf/6zSJKlnz55VdqxTUU+SiIiI+KwNB7IAaJEcc5o9S7PZbMeLN6RpyJ2IHKckSURERHzWhv1m0YbmtSueJAHUiTMXu9yXnldlMYmI71OSJCIiIj4pPaeAA5n5ADSvXbmhcnXiIwDYl5FbZXFJ1StvsQARqJrXi5IkERER8Ukb9pvzkerGRxATHlKpY9QtTpL2qifJK4WEmL/XnBwNh5Tyc71eXK+fylDhBhEREfFJrqINlZmP5OIebqeeJK9kt9uJj4/n4MGDgLmOUEWrGJaH0+mkoKCAvLy8EiWqpWpUV/sahkFOTg4HDx4kPj4eu91e6WMpSRIRERGftH5/VSRJruF26knyVsnJyQDuRMkTDMMgNzeXiIgIjyRhga662zc+Pt79uqksJUkiIiLik9w9SZUs2gCQEm/2JO1Nz8UwDH1A9kI2m406deqQlJSEw+HwyDkcDgfz5s2jS5cuZzRES8pWne0bEhJyRj1ILkqSRERExOcYhuGek1TZynYAycXD7fILnRzNcVAzKrRK4pOqZ7fbq+TD78mOXVhYSHh4uJIkD/DF9tWgSxEREfE5+zPzyMwrxB5ko0lSVKWPExZsJyHaTIz2pmtekoiYlCSJiIiIz3H1IjVKiCIs+Mx6F1LiNS9JREpSkiQiIiI+xzUfqbLrI51IFe5E5K+UJImIiIjP2XIwG4CmSZWfj+TiqnCntZJExEVJkoiIiPicLYeyAGiSWPn5SC6uCnfqSRIRFyVJIiIi4nOOJ0lnPtyudqyZJB3IVE+SiJiUJImIiIhPScsu4GiOuV5O4yroSUqMCQPg0LH8Mz6WiPgHJUkiIiLiU7YW9yLVjY8gMvTMl3xMijF7kg4qSRKRYkqSRERExKe4htpVRS8SHO9JOpZXSJ6jqEqOKSK+TUmSiIiI+JQth8zKdlUxHwkgNjyYsGDzI5GG3IkIKEkSERERH7PlYNVVtgOw2WwkxZq9SQePqXiDiChJEhERER+z9XDV9iQBJEareIOIHKckSURERHxGfmERO9NyAGiSVHVJkoo3iMiJlCSJiIiIz9h5JIcip0F0WDBJxQUXqoJ7uF2mkiQRUZIkIiIiPuT4IrJR2Gy2KjuuhtuJyImUJImIiIjPqOrKdi4q3CAiJ1KSJCIiIj7D3ZNUhfORQHOSRKQkJUkiIiLiM473JFVN+W8X14KyGm4nIqAkSURERHyEYRhsda+RVNU9SWaSdDgrnyKnUaXHFhHfoyRJREREfMKhY/kcyy8kyAb1a0VW6bFrRYdhs4HTgLTsgio9toj4HiVJIiIi4hM2F89HSq0ZSViwvUqPbQ+yUSMyFFCSJCIWJ0nPP/88HTt2JCYmhqSkJK666io2bNhQYp9u3bphs9lKfN11110WRSwiIiJW2X7YXES2cULVzkdyqRllJklHsjQvSSTQWZok/fLLL9x7770sWrSImTNn4nA46N27N9nZ2SX2u+OOO9i3b5/766WXXrIoYhEREbHKjiPm54MGtTycJKknSSTgBVt58hkzZpT4efz48SQlJbF06VK6dOni3h4ZGUlycnJ1hyciIiJeZNthM0lq5KGepIRo9SSJiMnSJOmvMjIyAKhZs2aJ7RMmTODTTz8lOTmZK6+8kn//+99ERpY9YTM/P5/8/ONvbpmZmQA4HA4cDoeHIi8f1/mtjsNfqX09S+3rWWpfz1L7elZ1te/24iQpNT7MI+eKjzA/Fh06lud1rxW9hj1L7etZ3tS+5Y3BZhiGV9S5dDqd9O/fn/T0dBYsWODe/t///pcGDRqQkpLCqlWr+L//+z/OP/98vv766zKPM3LkSEaNGlVq+8SJE0+aWImIiIh3cxrw2GI7DsPGv9sVkhBe9eeYviuIGbuD6FzbyaDGzqo/gYhYLicnh8GDB5ORkUFsbOxJ9/OaJOnuu+9m+vTpLFiwgHr16p10v59//pkePXqwefNmmjRpUur+snqSUlNTOXz48Ckbojo4HA5mzpxJr169CAkJsTQWf6T29Sy1r2epfT1L7etZ1dG++zLy6PLyPIKDbKx+sgfB9qqfVv3p4p2M+n49vc9K4u0b21b58c+EXsOepfb1LG9q38zMTBISEk6bJHnFcLv77ruP77//nnnz5p0yQQK44IILAE6aJIWFhREWFlZqe0hIiOW/FBdvisUfqX09S+3rWWpfz1L7epYn23d38ZD81JqRRISX/j9fFRJjIwBIzyn02teJXsOepfb1LG9o3/Ke39IkyTAM7r//fqZMmcLcuXNp1KjRaR+zYsUKAOrUqePh6ERERMRbuMp/N6ziRWRPVCvKTL4OZ6twg0igszRJuvfee5k4cSLffPMNMTEx7N+/H4C4uDgiIiLYsmULEydOpF+/ftSqVYtVq1bx8MMP06VLF8455xwrQxcREZFq5Ony3wC1orWYrIiYLE2Sxo4dC5gLxp5o3LhxDBs2jNDQUGbNmsWYMWPIzs4mNTWVa6+9lieeeMKCaEVERMQqni7/DcfXSUrPcVBY5PTIvCcR8Q2WD7c7ldTUVH755ZdqikZERES81fbinqSGHkySakSGYrOBYUBaTgFJMR4ooSciPkGXSERERMSrOZ0GO454fk6SPchGjUgNuRMRJUkiIiLi5fZn5pFf6CQ4yEbd+AiPnqtW8ZC7I1lKkkQCmZIkERER8WquoXapNSM9Pk/INS/piHqSRAKakiQRERHxatVR/tvFXeEuS2XARQKZkiQRERHxatVRtMHFtVaSepJEApuSJBEREfFq24vLfzf04BpJLhpuJyKgJElERES8XLX2JEW7CjdouJ1IIFOSJCIiIl6rusp/u7iG26kEuEhgU5IkIiIiXqs6y3+DhtuJiElJkoiIiHgt13yk6ij/DVAjKgSA9ByHx88lIt5LSZKIiIh4re3VONQOoEak2ZOUnlOA02lUyzlFxPsoSRIRERGvVZ1FGwDiIsyeJKcBx/ILq+WcIuJ9lCSJiIiI16rO8t8A4SF2IkLsgNmbJCKBSUmSiIiIeK3q7kkCqBFp9iYd1bwkkYClJElERES8UnWX/3aJL56XdFQ9SSIBS0mSiIiIeKXqLv/tcrzCnZIkkUClJElERES8kms+Uv1qKv/tEu+ucKfhdiKBSkmSiIiIeCVX+e8G1TjUDiA+QnOSRAKdkiQRERHxSlYUbYCSayWJSGBSkiQiIiJeqbrLf7vEq7qdSMBTkiQiIiJeST1JImIVJUkiIiLidawq/w0nVrdTT5JIoFKSJCIiIl7HqvLfoHWSRERJkoiIiHghq8p/w/HqdupJEglcSpJERETE61hV/huOz0nKyi+koNBZ7ecXEespSRIRERGvY1XRBoDYiBBsNvN2eq6G3IkEIiVJIiIi4nW2WVT+G8AeZCOueMhdhobciQQkJUkiIiLidXZY2JMEx4fcaa0kkcCkJElERES8yonlvxtZ0JMEuHuSVOFOJDApSRIRERGvcmL575T4cEtiqBHpqnCnJEkkEClJEhEREa9iZflvFw23EwlsSpJERETEq2wrno9kRflvF9eCslorSSQwKUkSERERr+Kaj2RV0QbQcDuRQKckSURERLyKleW/XeIjVbhBJJApSRIRERGvYnX5bzg+3E5zkkQCk5IkERER8RreUP4bjhdu0HA7kcCkJElERES8hjeU/4bjw+1UuEEkMClJEhEREa/hDeW/oWSSZBiGZXGIiDWUJImIiIjX2F481M7K8t8AcRFmklRQ5CTP4bQ0FhGpfkqSRERExGts94KiDQDRYcHYg2wAZORqyJ1IoFGSJCIiIl5juxeU/waw2Wzu3iQlSSKBR0mSiIiIeA1v6UkClCSJBDAlSSIiIuIVTiz/3dDiOUkAsUqSRAKWkiQRERHxCieW/64bH2F1OOpJEglgSpJERETEK7jmI6VaXP7bxZUkaUFZkcBj/TuQiIiICMfLf3vDUDuAuIhgADLVkyQScJQkiYiIiFdwFW1oYHFlOxcNtxMJXEqSRERExCu4hts18oLKdgDxEaGAkiSRQKQkSURERLzC8Z4kbxlup54kkUClJElEREQsd2L5b2/pSVIJcJHApSRJRERELOdt5b9BPUkigUxJkoiIiFjONdTOW8p/w4lJUqHFkYhIdfOOdyEREREJaNsPm0PtvGU+EkBcpCtJKsAwDIujEZHqpCRJRERELLejuCepoZeU/4bjPUmOIoNcR5HF0YhIdVKSJCIiIpbbdtiVJHlPT1JUqJ3gIBugeUkigUZJkoiIiFjOVdmuoZdUtgOw2Wwq3iASoJQkiYiIiKWcTsNduMGbhtvBCcUbcpQkiQQSJUkiIiJiqQPHjpf/rlfDO8p/u2itJJHApCRJRERELOWaj1SvRoTXlP920XA7kcDkXe9EIiIiEnC8cT6Si5IkkcCkJElEREQstf2wd85HAiVJIoHK0iTp+eefp2PHjsTExJCUlMRVV13Fhg0bSuyTl5fHvffeS61atYiOjubaa6/lwIEDFkUsIiIiVe140QbvKf/toiRJJDBZmiT98ssv3HvvvSxatIiZM2ficDjo3bs32dnZ7n0efvhhvvvuOyZPnswvv/zC3r17ueaaayyMWkRERKrS9sPeO9wuPlJJkkggCrby5DNmzCjx8/jx40lKSmLp0qV06dKFjIwMPvjgAyZOnMill14KwLhx42jVqhWLFi3iwgsvtCJsERERqSJOp8GONO8dbqfqdiKBydIk6a8yMjIAqFmzJgBLly7F4XDQs2dP9z4tW7akfv36/Pbbb2UmSfn5+eTn57t/zszMBMDhcOBwWPsG5zq/1XH4K7WvZ6l9PUvt61lqX886k/bdl5FHnsMs/107OtjrfkfRIeagm/ScAktj02vYs9S+nuVN7VveGGyGYRgejqVcnE4n/fv3Jz09nQULFgAwceJEbr311hJJD8D5559P9+7defHFF0sdZ+TIkYwaNarU9okTJxIZ6X1jnUVERALZpgwbb62zkxBu8O92RVaHU8rmDHhzXTBJ4Qb/8sL4RKRicnJyGDx4MBkZGcTGxp50P6/pSbr33ntZs2aNO0GqrMcff5wRI0a4f87MzCQ1NZXevXufsiGqg8PhYObMmfTq1YuQkBBLY/FHal/PUvt6ltrXs9S+nnUm7fv5kt2wbh2tUxPp16+9hyKsvPX7j/Hmut8osofRr183y+LQa9iz1L6e5U3t6xpldjpekSTdd999fP/998ybN4969eq5tycnJ1NQUEB6ejrx8fHu7QcOHCA5ObnMY4WFhREWFlZqe0hIiOW/FBdvisUfqX09S+3rWWpfz1L7elZl2nfX0TwAGiVGe+XvplZMBGDOSQoODsZms1kaj17DnqX29SxvaN/ynt/S6naGYXDfffcxZcoUfv75Zxo1alTi/g4dOhASEsLs2bPd2zZs2MDOnTvp1KlTdYcrIiIiVcyby3/D8RLghU6DnAINtxMJFJb2JN17771MnDiRb775hpiYGPbv3w9AXFwcERERxMXFcdtttzFixAhq1qxJbGws999/P506dVJlOxERET/gKv/dwAvLfwNEhtoJDrJR6DTIyHUQFeYVg3BExMMs/UsfO3YsAN26dSuxfdy4cQwbNgyA1157jaCgIK699lry8/Pp06cP77zzTjVHKiIiIlXN6TTcPUmNvLD8N4DNZiM+MoTDWQVk5DpIiY+wOiQRqQaWJknlKawXHh7O22+/zdtvv10NEYmIiEh12ZeZR36hkxC7jXo1vDf5iI04niSJSGCwdE6SiIiIBK6th7IAqF8zkmC7934kidOCsiIBx3vfkURERMSvbTtcPNQuIdriSE5NSZJI4FGSJCIiIpbYeshMkhoneud8JBdXkpSpJEkkYChJEhEREUu4epIae2llOxdXkpSeoyRJJFAoSRIRERFLbD1szklq5CNJkobbiQQOJUkiIiJS7fILi9h9NBeARj4y3E5JkkjgUJIkIiIi1W7nkRwMA2LCgkmMDrM6nFNSkiQSeJQkiYiISLXbUly0oVFiFDabzeJoTk1JkkjgUZIkIiIi1e54+W/vHmoHqm4nEoiUJImIiEi121ZctKGxl6+RBBAXqZ4kkUCjJElERESq3dYThtt5uxOH2xmGYXE0IlIdlCSJiIhItfOVNZIAYsPNJKnQaZBTUGRxNCJSHZQkiYiISLXKyHFwJLsA8I05SZGhdoKDzOISGnInEhiUJImIiEi12nbE7EWqHRtGVFiwxdGcns1mU4U7kQCjJElERESq1dZDZtEGX+hFclGSJBJYlCSJiIhItTpe/tv7K9u5xKoMuEhAUZIkIiIi1WprcZLUxAcq27moJ0kksChJEhERkWrlLv+t4XYi4qWUJImIiEi1cToNth/23SRJw+1EAoOSJBEREak2B47lkesoIjjIRmrNSKvDKTf1JIkEFiVJIiIiUm22FQ+1q18zkhC773wMUZIkElh8591JREREfN4WHxxqB0qSRAKNkiQRERGpNq6epMY+VNkOjpcAV5IkEhiUJImIiEi12XbYtZCs76yRBOpJEgk0SpJERESk2mzxwfLfALERwQBk5BZaHImIVAclSSIiIlIt8hxF7DqaA0DTJN/sScrMdWAYhsXRiIinKUkSERGRarH1UDaGYSYcCdGhVodTIa4kqaDISZ7DaXE0IuJpSpJERESkWmw5ZM5HapoUjc1msziaiokOC8YeZMaseUki/k9JkoiIiFSLzQfNJKmJj1W2A7DZbMSGm/OSMvOUJIn4OyVJIiIiUi1O7EnyRapwJxI4lCSJiIhItTjek+TjSVKOkiQRf6ckSURERDyuyGmw9bBZ/ttXe5K0oKxI4FCSJCIiIh6352guBYVOQoODqFcj0upwKkXD7UQCh5IkERER8bjNh44B0Dghyl0lztcoSRIJHEqSRERExOPc85F8dKgdKEkSCSRKkkRERMTjthw05yP5atEGOJ4kZSpJEvF7SpJERETE4zb7ePlvUOEGkUCiJElEREQ8yjAM93C7pn7Qk6QkScT/KUkSERERjzqSXUBGrgObDRonRlkdTqUpSRIJHJVKkg4cOMDNN99MSkoKwcHB2O32El8iIiIiLq5epHo1IggP8d3PCUqSRAJHcGUeNGzYMHbu3Mm///1v6tSpg83mm6U8RURExPO2FM9H8uWiDaAkSSSQVCpJWrBgAfPnz6dt27ZVHI6IiIj4G3+YjwTHCzfkFzrJcxT5dK+YiJxapYbbpaamYhhGVcciIiIifsidJPlwZTuAmLBgXINnMvPUmyTizyqVJI0ZM4Z//OMfbN++vYrDEREREX+z9VDxGkk+niQFBdmIDddaSSKBoFLD7a6//npycnJo0qQJkZGRhISElLg/LS2tSoITERER35adX8ie9FzA94fbgTkvKSPXoXlJIn6uUknSmDFjqjgMERER8UeuXqSaUaHUiAq1OJozp+INIoGhUknS0KFDqzoOERER8UObDx0D/KMXCZQkiQSKSiVJAEVFRUydOpU///wTgNatW9O/f3+tkyQiIiJuG/abRRua1fazJClHSZKIP6tUkrR582b69evHnj17aNGiBQDPP/88qampTJs2jSZNmlRpkCIiIuKbNh0we5JaJMdYHEnViHX3JBVaHImIeFKlqts98MADNGnShF27drFs2TKWLVvGzp07adSoEQ888EBVxygiIiI+auNBM0lqluQvSZJ5fVnD7UT8W6V6kn755RcWLVpEzZo13dtq1arFCy+8QOfOnassOBEREfFdOQWF7EozK9s197fhdkqSRPxapXqSwsLCOHbsWKntWVlZhIb6fuUaEREROXOuRWQTokOpFR1mcTRVQ0mSSGCoVJJ0xRVXcOedd7J48WIMw8AwDBYtWsRdd91F//79qzpGERER8UEb9vvXUDs4niRpMVkR/1apJOmNN96gSZMmdOrUifDwcMLDw+ncuTNNmzbl9ddfr+oYRURExAdtKu5J8pehdnBCkpSnJEnEn1VqTlJ8fDzffPMNmzZtYv369QC0atWKpk2bVmlwIiIi4rs2Fle2a1bb/3qSNNxOxL9Vep0kgGbNmtGsWbOqikVERET8yKYDZk+Sv5T/BiVJIoGi3EnSiBEjeOaZZ4iKimLEiBGn3PfVV18948BERETEdx3Lc7AnvbiynR/OScopKMJR5CTEXqmZCyLi5cqdJC1fvhyHw+G+LSIiInIyrvlISTFhxEWGWBxN1YkJP/5cMnIdJPhJ1T4RKancSdKcOXPKvC0iIiLyV5uK5yP501A7AHuQjZjwYI7lFSpJEvFjleojHj58eJnrJGVnZzN8+PAzDkpERER828bi+Uj+VP7bRfOSRPxfpZKkjz76iNzc3FLbc3Nz+fjjj884KBEREfFtrsp2/lT+20VJkoj/q1CSlJmZSUZGBoZhcOzYMTIzM91fR48e5YcffiApKancx5s3bx5XXnklKSkp2Gw2pk6dWuL+YcOGYbPZSnz17du3IiGLiIiIBfyx/LeLFpQV8X8VKgEeHx/vTlaaN29e6n6bzcaoUaPKfbzs7GzOPfdchg8fzjXXXFPmPn379mXcuHHun8PCNPZXRETEm2XkOjiQmQ/4Z09SbLh6kkT8XYWSpDlz5mAYBpdeeilfffUVNWvWdN8XGhpKgwYNSElJKffxLrvsMi677LJT7hMWFkZycnJFwhQRERELuYo2pMSFl6gG5y/cw+1ylCSJ+KsKJUldu3YFYNu2baSmphIU5Pm1AebOnUtSUhI1atTg0ksvZfTo0dSqVeuk++fn55Ofn+/+OTMzEwCHw+EuYW4V1/mtjsNfqX09S+3rWWpfz1L7etZf23fd3nQAmiZF+WWbR4eZn3+OZudX2/PTa9iz1L6e5U3tW94YbIZhGJU9SU5ODjt37qSgoKDE9nPOOafCx7LZbEyZMoWrrrrKvW3SpElERkbSqFEjtmzZwj//+U+io6P57bffsNvtZR5n5MiRZQ75mzhxIpGRkRWOS0RERCrmy61BzD8QxKV1nAxo6LQ6nCr3024b03bZuSDRyeCm/vf8RPxZTk4OgwcPJiMjg9jY2JPuV6kk6dChQ9x6661Mnz69zPuLiooqesgyk6S/2rp1K02aNGHWrFn06NGjzH3K6klKTU3l8OHDp2yI6uBwOJg5cya9evUiJMT/hh9YTe3rWWpfz1L7epba17P+2r43vv87S3ak8/K1bRjQtvzD8H3FhN93MfK7P+nVKol3BretlnPqNexZal/P8qb2zczMJCEh4bRJUoWG27k89NBDpKens3jxYrp168aUKVM4cOAAo0eP5pVXXql00KfTuHFjEhIS2Lx580mTpLCwsDKLO4SEhFj+S3Hxplj8kdrXs9S+nqX29Sy1r2eFhIQQHBzMhuI1klrXq+GX7V0zOhyAY/mF1f789Br2LLWvZ3lD+5b3/JVKkn7++We++eYbzjvvPIKCgmjQoAG9evUiNjaW559/nssvv7wyhz2t3bt3c+TIEerUqeOR44uIiMiZ2ZuRx7G8QkLsNpok+l9lOzhxnaRCiyMREU+pVOWF7Oxs93pINWrU4NChQwCcffbZLFu2rNzHycrKYsWKFaxYsQIwC0KsWLGCnTt3kpWVxaOPPsqiRYvYvn07s2fPZsCAATRt2pQ+ffpUJmwRERHxsD/3mgWTmiRGExrs+QJPVtA6SSL+r1LvXi1atGDDhg0AnHvuubz33nvs2bOHd999t0K9PEuWLKFdu3a0a9cOgBEjRtCuXTuefPJJ7HY7q1aton///jRv3pzbbruNDh06MH/+fK2VJCIi4qXW7zeTpFZ1rJ0H7EnHe5KUJIn4q0oNt3vwwQfZt28fAE899RR9+/ZlwoQJhIaGMn78+HIfp1u3bpyqbsSPP/5YmfBERETEIn/uN9dIapkcY3EknuNKkrLyCykschJs988eM5FAVqkk6aabbnLf7tChAzt27GD9+vXUr1+fhISEKgtOREREfMuf+8yepJZ+3JMUG37841NmXiE1o0ItjEZEPKFSSdJfRUZG0r59+6o4lIiIiPio3IIith/OBqCVH/ckBduDiA4LJiu/kIxch5IkET9U7iRpxIgR5T7oq6++WqlgRERExHdtPpSF04BaUaEkxvj3/OG4iBB3kiQi/qfcSdLy5cvLtZ/NZqt0MCIiIuK71u8310dqWSfG7z8PxBQPuVOSJOKfyp0kzZkzx5NxiIiIiI9b7y7a4L/zkVxU4U7Ev6kci4iIiFSJDQf8v7Kdi5IkEf9WqcIN3bt3P2U3+s8//1zpgERERMT3GAZsKB5u589rJLloQVkR/1apJKlt27YlfnY4HKxYsYI1a9YwdOjQqohLREREfEhGAaTnOrAH2WiaFG11OB6nJEnEv1UqSXrttdfK3D5y5EiysrLOKCARERHxPXtyzBEmjROiCA+xWxyN52m4nYh/q9I5STfddBMffvhhVR5SREREfMDeHPO7Py8ie6K4SCVJIv6sSpOk3377jfDw8Ko8pIiIiPiAvdlmT1IgFG0A9SSJ+LtKDbe75pprSvxsGAb79u1jyZIl/Pvf/66SwERERMR37C5OklqnBEZPUqySJBG/VqkkKS4ursTPQUFBtGjRgqeffprevXtXSWAiIiLiG7LyCzmUZ95unRJ36p39hHqSRPxbpZKkcePGVXUcIiIi4qPW7z+GgY3aMWEkxoRZHU61UJIk4t8qlSS5LFmyhD///BOAs846iw4dOlRJUCIiIuI71u0zF5E9KyUw5iPB8STpWF4hRU4De9DJ148UEd9TqSRp9+7d3Hjjjfz666/Ex8cDkJ6ezkUXXcSkSZOoV69eVcYoIiIiXmzt3kwAWgdIZTuA2PAQ9+3MXAc1okItjEZEqlqlqtvdfvvtOBwO/vzzT9LS0khLS+PPP//E6XRy++23V3WMIiIi4sXWFSdJZwVQkhQaHER0mHmtOV1D7kT8TqV6kn755RcWLlxIixYt3NtatGjBm2++ySWXXFJlwYmIiIh3y3MUsflQNgCtA2i4HZhD7rLyCzmaU0AjoqwOR0SqUKV6klJTU3E4Sl81KSoqIiUl5YyDEhEREd+w8cAxCp0GUcEGdeICa63EGlHmkLv0nAKLIxGRqlapJOk///kP999/P0uWLHFvW7JkCQ8++CAvv/xylQUnIiIi3m3NHnOoXb0oA5stsIoX1Ig05yEdzdZwOxF/U6nhdsOGDSMnJ4cLLriA4GDzEIWFhQQHBzN8+HCGDx/u3jctLa1qIhURERGvs3ZvBgD1AnC0WbwrSVJPkojfqVSSNGbMmCoOQ0RERHzRmr3He5ICTY1IrZUk4q8qlSQNHTq0quMQERERH1NY5GT9vsBNktSTJOK/Kr2YbFFREVOnTnUvJtu6dWv69++P3W6vsuBERETEe205lE1+oZOoMDsJ4YVWh1PtXD1JR3PUkyTibyqVJG3evJl+/fqxZ88edxnw559/ntTUVKZNm0aTJk2qNEgRERHxPmv2mPORWiXHEGTLtzia6hcfqep2Iv6qUtXtHnjgAZo0acKuXbtYtmwZy5YtY+fOnTRq1IgHHnigqmMUERERL7S2eD5S65TAWUT2RPGqbifityq9mOyiRYuoWbOme1utWrV44YUX6Ny5c5UFJyIiIt5rTXFlu7PqxMA+i4OxgKsEuHqSRPxPpXqSwsLCOHbsWKntWVlZhIaGnnFQIiIi4t2KnIZ7uF2bAO1J0pwkEf9VqSTpiiuu4M4772Tx4sUYhoFhGCxatIi77rqL/v37V3WMIiIi4mW2HMoip6CIyFA7TRKjrQ7HEq7hdrmOIvIcRRZHIyJVqVJJ0htvvEHTpk256KKLCA8PJzw8nM6dO9O0aVNef/31qo5RREREvMyKXekAtKkbhz3IZm0wFokND3Y/d62VJOJfKjQnyel08p///Idvv/2WgoICrrrqKoYOHYrNZqNVq1Y0bdrUU3GKiIiIF1m1Ox2AtqnxlsZhJZvNRlxECGnZBRzNKaB2bLjVIYlIFalQkvTss88ycuRIevbsSUREBD/88ANxcXF8+OGHnopPREREvNDKXeZ8pHPrxVsbiMXiI4uTJFW4E/ErFRpu9/HHH/POO+/w448/MnXqVL777jsmTJiA0+n0VHwiIiLiZfIcRazfb5b/PqdenMXRWEsV7kT8U4WSpJ07d9KvXz/3zz179sRms7F3794qD0xERES805/7MnEUGdSKCqVejQirw7GUKtyJ+KcKJUmFhYWEh5ccbxsSEoLDoTcGERGRQLFqtznU7px6cdhsgVm0wcW9oKx6kkT8SoXmJBmGwbBhwwgLC3Nvy8vL46677iIqKsq97euvv666CEVERMSrrCyubHduABdtcHH1JGm4nYh/qVCSNHTo0FLbbrrppioLRkRERLzfiuLKdoFetAGO9ySla7idiF+pUJI0btw4T8UhIiIiPiAzz8HWQ9mAijaAWd0ONCdJxN9UajFZERERCUyri+cj1asRQa3osNPs7f9U3U7EPylJEhERkXJb6Rpqp/lIwIk9SUqSRPyJkiQREREpN1fRhraajwSc2JOk4XYi/kRJkoiIiJTbieW/5YQkKdeBYRgWRyMiVUVJkoiIiJTLvoxc9mXkEWSDNnWVJMHx4XZFToPMvEKLoxGRqqIkSURERMpl2Y50AFrViSUqrEIFcv1WeIidiBA7ABkacifiN5QkiYiISLks3XEUgA4NalgciXdR8QYR/6MkSURERMpl2U4lSWVxLSirJEnEfyhJEhERkdPKcxSxdq9ZtKF9fSVJJ6pR3JOkCnci/kNJkoiIiJzW6j0ZOIoMEmPCqFcjwupwvEoN9SSJ+B0lSSIiInJay1zzkerXwGazWRyNdzk+J0k9SSL+QkmSiIiInJaraEP7BvHWBuKFakYV9yRlqydJxF8oSRIREZFTMgxDRRtOwZUkpSlJEvEbWuRARERETmlXWi6HswoItQfROkWLyP6VK0k6nJV/+p0NA7IPg1EEUYkQZPdwdCJSGUqSvEleBqz7Bnb/AbnpEBEPdTtAsz4QW8fq6EREJEAt3ZkGQOu6sYSH6EP9XyVEhwGn6Uk6sgV+HQN/fg+5ZnsSGg2p50PbIdCqPwSHej5YESkXJUnewFkEi96BuS9AQVbJ+5Z9DDY7tLkGuj0OtZpYE6OIiASspScUbZDSTjnczjBg4ZswexQ4C4s3Fhe+KMiCLT+bXzUbQ5/noMVl1RO0iJySkiSrFWTDl8Nh4wzz58SW0PJyiKkDx/bD1jmwZymsngx/fgdd/w8uegDs+tWJiEj1WLojHYD2mo9UplpRx0uAO50GQUHFSZBhwA+Pwh//M39u2hM6PwipF0BQMBxaD+u+hSUfQNpW+OwGaH8L9H0BQqMsejYiAkqSrFXkgM9vMq8gBYfDZS+Zb44nllbt8W/YuwJmPQVb55pXorbOhevGQVQtiwIXEZFAkZHrYP3+TADOa6gkqSw1ipMkpwHpuQ53zxK/vFScINnM//Hn31Hyf3zt1ubXRfeZ+y580xxBsnsJDJkMcfWq/8mICKDqdtb68V9mghQSCbd8Ax2GlnzzdElpCzdPhavGQkgUbPsF/tsNDm+q5oBFRCTQLN2RhmFA44QokmLCrQ7HK4XYg4gNN687p2UXF2/Y+gvMfd68fcVrcMGdZf+PBwiLgd7PwNDvILo2HFwH7/eEfauqIXoRKYuSJKts+Rl+f8+8fe0HUP/CU+9vs0HbwXDHbHPccsZO+LCP2cskIiLiIYu3mUUGzm9U0+JIvJureMPhrALIPwZf3wkY0O5mOO/W8h2k0SVw+2xIbAXH9sH4K2DPMs8FLSInpSTJCgXZ8M395u2Od0DLfuV/bFIruG0m1DkXco6Yb6C7l3omThERCXi/K0kqlxLFG+a9DFn7zYual71UsQPFp8LwGVC/E+RnwCdX6YKoiAWUJFkg6Pd3IXM3xDeAXqMqfoCoBBj6PTToDAXH4NNrYP+aqg9UREQCWk5BIat3ZwBKkk7HlSTlH9piVqwF6PM8hEZW/GAR8eacpNQLzeVBPh4AB9ZWXbAiclpKkqpZqCOToN/eNH/o8WTlq9eEx8LgL6BeR8hLN680Hd5cVWGKiIiwfGc6hU6DuvER1KtRiQ/7AaRWtJkkNd/wHhQVQJMe0LxP5Q8YFmMmSsX/54M/v4HwgiNVFK2InI6lSdK8efO48sorSUlJwWazMXXq1BL3G4bBk08+SZ06dYiIiKBnz55s2uTbxQqaHpyOrSAL6rSF1tec2cHCos030OSzIfuQ2aOUdahK4hQREdF8pPKrGRVKMkdocfAHc0O3x09eqKG8wmPN//MJLbAd20enLS+bPUsi4nGWJknZ2dmce+65vP3222Xe/9JLL/HGG2/w7rvvsnjxYqKioujTpw95eXnVHGkVKcii4ZE55u2u/wdBVdD8ETXgpilQoyGk74BJN4Ij98yPKyIiAe/3bWbPhZKk06sZFcbtwT8QbBRCw0sgtWPVHDiiBtz0JUZ0bWLz9mD/8hYoLGPRWhGpUpYmSZdddhmjR4/m6quvLnWfYRiMGTOGJ554ggEDBnDOOefw8ccfs3fv3lI9Tr7i8ILxhBTl4KzRGJr3rboDRyfCkC8hPB52/wFT7gKns+qOLyIiASe/sIjlO9MBJUnlUTu8iEH2X8wfOj9UtQePr0/h9ZMoDAonaMev8MMj5kK1IuIxXruY7LZt29i/fz89e/Z0b4uLi+OCCy7gt99+44Ybbijzcfn5+eTn57t/zsw0F8BzOBw4HA7PBn0qziIcC8eCDQ60HEpCUREUFVXd8eMaYrvuI+wTr8O2bipFsxri7P5E1R3fB7h+v5b+nv2Y2tez1L6epfatuOU7jpJf6KRWVCipcaGnbDu1LzQ5OJNYWw77bLVJaHAJVHFbOGq1ZGXDe7lw66vYln1EUVJrnB2GV+k5Aplew57lTe1b3hi8Nknav38/ALVr1y6xvXbt2u77yvL8888zalTpinE//fQTkZHWTToNKTxGBKlEGTlM3p9Cgx9+8Mh56qUOp8OO97AvHMOyvQ721rjAI+fxZjNnzrQ6BL+m9vUsta9nqX3Lb+YeG2AnNSyP6dOnl+8xAdy+Hf/8AIAvirrScPoMz5wk7lzWpQyi9d7Psc14nMWb0zkS09Iz5wpQgfwarg7e0L45OTnl2s9rk6TKevzxxxkxYoT758zMTFJTU+nduzexsbEWRgaD9jVg0+79jG7TkX7n1PXQWfpR9HMY9t/e4Lw94yjsOQhqt/bQubyLw+Fg5syZ9OrVi5CQEKvD8TtqX89S+3qW2rfivhi/FDjClZ3Oot+F9U+5b8C375FNhCzfRKERxKTCrsztexlBQWdYtOEvXG3caMhrOH9wELT2azrvfY/CW2dC/Kl/P3J6Af8a9jBval/XKLPT8dokKTk5GYADBw5Qp04d9/YDBw7Qtm3bkz4uLCyMsLCwUttDQkIs/6UkxISznEjS85yejaXXSDi4BtuWnwn58ha4cy5EBs54cm/4Xfszta9nqX09S+1bPvmFRSzdeRSAS5onlbvNArZ9138HwHzn2exz1iC3COLDPNMOIaGhBA14G9K2YNu3kpAvh8JtP1VuPSYpJWBfw9XEG9q3vOf32nWSGjVqRHJyMrNnz3Zvy8zMZPHixXTq1MnCyCrPtYbCkaz80+x5hoLscO0H5mK16Tvgq9vAWYXzn0RExK8t35lOnsNJQnQYzZKirQ7H+639GoBZ9s4AHMn2cPW50Ei4YSJEJcKB1TDt7yrkIFLFLE2SsrKyWLFiBStWrADMYg0rVqxg586d2Gw2HnroIUaPHs23337L6tWrueWWW0hJSeGqq66yMuxKSyhejftwVjWU7oysab6BhkTClp9h9tOeP6eIiPiFhZsPA3BRk1rYznStH393YB0cWg/2UFZGXgTAker4Px9XD64bB7YgWDkRln3k+XOKBBBLk6QlS5bQrl072rVrB8CIESNo164dTz75JACPPfYY999/P3feeScdO3YkKyuLGTNmEB4ebmXYlZYQXY1JEkByGxhQvAbVr2Ng3bfVc14REfFpC7eY6yN1blrL4kh8wNop5vcmPQiNNoe2p2V7eMSIS6NLoIf5mYkfHoW9y6vnvCIBwNIkqVu3bhiGUepr/PjxANhsNp5++mn2799PXl4es2bNonnz5laGfEZqFvckebwb/kRtroFO95m3v7kXjmypvnOLiIjPyc4vZMWudAAuapJgbTC+YGNx5b+z+lMzypwTXa3/5zs/BC36QVEBfHEL5KRV37lF/JjXzknyRwnRxW+e1dWT5NJzJNTvBPmZ5huoI7d6zy8iIj7j921pFDoNUmtGkFpTxQBOKXMv7F8N2KBZb2oVXwxNq87/8zYbXDUWajSE9J0w5W9aUF6kCihJqkbu4XbV1Q3vYg8xxy1HJcKBNTDtkeo9v4iI+IyFW4rnIzVWL9JpbfrJ/F63A0QlUDPaghEjABHxMOgTCA43Y1rwSvWeX8QPKUmqRq4kKTu/iDxHNVebi60D131oTvBc8Sks+6R6zy8iIj7h183mfKSLNB/p9DYWJ0nN+wC4e5KqPUkCqHMO9HvZvD3nOdgyp/pjEPEjSpKqUXRYMHabWaLz0LFq7k0CaNQFLn3CvP3DI7BvVfXHICIiXistu4B1+8yFFjUf6TQK82HrXPN2s94AJMaYw+oPW/E/HqD9zdDuZjCc5vIfGXusiUPEDyhJqkY2m42Y4vWrLLnKBND5YWjeFwrz4IubITfdmjhERMTrLNpq9iI1rx3t/sAvJ7HjV3BkQ3Qy1DkXOJ4kHfL0eoin0u8/kHw25ByBycOg0KLPGyI+TklSNXMlSZZdZQoKMid4xteHo9th6j1agE5ERACYv+kQAJ2bqhfptFy9SE17mMUTgCRXkmTV/3iAkAhzflJYHOz+HWY+aV0sIj5MSVI1iwkxE5LDVl5liqwJAz8CeyhsmAYL37AuFhER8QqGYfDLBjNJ6to80eJofMC2eeb3Rl3dmxKjzXUcM3Id5BdW89zjE9VsBFe/a95ePBbWfGVdLCI+SklSNXP3JFmZJAHUbQ+XvWjenjUKtv9qbTwiImKpLYey2JuRR2hwEBc0UtGGU8pNh30rzduNLnFvjo0IJtRufrSqtoXjT6ZlP7j4YfP2tw/AoY3WxiPiY5QkVbPjSZIXjBHucCuccz0YRfDlrXDsgNURiYiIReYW9yJd0KgmEaF2i6PxcjsWmsURajWF2BT3ZpvN5q5ka+mQO5fuT0DDS6Agy5yHnJ9ldUQiPkNJUjWLCfWC4XYuNhtc8RoktoKsA2YlnKJCq6MSEREL/LJRQ+3Kbft883ujLqXuSvSGeUku9mBz+Y/oZDi0Hr57UPOQRcpJSVI185rhdi6hUXD9JxAabb7p//yM1RGJiEg1yy0oYvG2NAC6tVCSdFqu+UgNLyl1l1clSQDRSTBwPNjssOZL+ON9qyMS8QlKkqpZtDcNt3NJaAYD3jJv/zoG/vzO0nBERKR6Ld52hIJCJylx4TRJjLY6HO+WfQQOrDFv+0KSBNCgE/Quvgg643HYvcTaeER8gJKkauYV1e3K0vpq6HSfeXvK3ZrgKSISQNxD7VokYisuZy0nsWuR+T2xJUSX7nVLjHatlZRXnVGd3oX3wFkDwOmAL4aayZ6InJSSpGoWV9yTlJ5jcXnQsvQcCQ06Q8Ex+PwmTfAUEQkQmo9UATuLk6TUC8q82yt7ksCch9z/LbPYROZu+Pp2cHrZ5xARL6IkqZpFBkOI3bxK53VvoPYQc9xyTB04vAG+uVcTPEVE/NyutBy2HsrGHmTjIi0ie3q7Fpvf619Y5t2uJMmrhtW7hMeaC82GRMKWn+GXl6yOSMRrKUmqZjbb8RW5D2R6WZIE5gTPQR9DUAismwq/vWV1RCIi4kFzNhwEoEP9GsSGh1gcjZcrzIe9y83bvtaT5FL7LLhijHn7lxdh0yxLwxHxVkqSLJDkfgP1svHKLqnnQ9/nzdszn4Jt862NR0REPGbmOnONvB6tkiyOxAfsXQFFBRCZADUbl7lLQvTxJMnw1tEY514P5w0HDHPYXfpOqyMS8TpKkiyQ6M09SS4db4dzbji+0GzGHqsjEhGRKpaVX8jirWbp7x6talscjQ9wFW2of6E5NKQMriQp11FEdoEXz/np+wKktIPco/DFLeDw0gu3IhZRkmSB2u4kyYvfkFwLzdY+G7IPweSh5jADERHxG/M3HqKgyEmjhCiaJEZZHY7321k8H+kkQ+0AosKCiQq1A1485A4gOMwcXh9RwxxC+P3DmocscgIlSRZwDbc76M1vngChkXD9xxAeB7v/MNdWEBERvzHzz+Khdi2TVPr7dAzjtEUbXLx+XpJLfH24bhzYgmDlRFj8ntURiXgNJUkWSPSFniSXmo3hmuLVuZd8AEvHWxqOiIhUjSKnwZz1ZtEGDbUrhyNbIOcw2MOgzrmn3NVnkiSAJt2h92jz9o//hK2/WBuPiJdQkmSBpFgfevMEaN4buj9h3p72COxYaG08IiJyxpbtPMrRHAdxESGc17CG1eF4vz1LzO8pbc2haqeQ6B4x4gMXQ8FcaNY1D3nyMDi63eqIRCynJMkCSdE+1JPk0uUROOsqc6Xuz29WJRwRER83q3ioXbcWiYTY9XHgtPYsM7+ntD/trrVjwwHY7yv/5202uHJMcSGHNJg0BAqyrY5KxFJ6V7SAqyfpaI6D/EIvrnxzIpsNrnoHks82hxt8NlhvoCIiPmz2n+ZQu54aalc+rvWR6p4+SUouTpIOZPhIkgQQEgHXT4CoJDiwBqbeo0IOEtCUJFkgPiKE0OKrdj4z5A4gNApu+MxcH+LAaph6t95ARUR80NZDWWw+mEVwkI0uzROtDsf7FTlg/yrzdjl6kpLjzCRpny8lSQBxdeH6T44vKL/gVasjErGMkiQL2Gw231grqSzxqXD9p8VvoN/AvP9YHZGIiFTQ9DX7AbioaQJxESEWR+MDDv4JhXkQFnfSRWRP5Bpu51PD6l3qXwj9iv+3z34GNky3Nh4RiyhJskjt4iF3B33xDbRBJ7ii+OrSnGfhz++sjUdERCpk+pp9AFzWJtniSHzEXtd8pLYQdPqPTsknzEkyfHHExXm3wnnDAQO+vA32rbI6IpFqpyTJIkkx5huo16+VdDLtb4EL7jJvf30n7F1haTgiIlI+O4/ksGZPJkE26H2W5iOVi7toQ7ty7e4abpfncJKZW+ipqDzrspegcTdwZMPE6yFzn9URiVQrJUkWcfUk+WRXvEvvZ6Fxd3DkmG+gGbutjkhERE5jxlrzw+6FjWtRK/rUpaylWAWKNgCEh9iJjzSHMfpMhbu/sofAwI8goTkc2wuf3aCCTRJQlCRZJCnWx3uSAOzBMOgjSGwFWfthwiDIy7Q6KhEROYUfVpvzkTTUrpwceXBwnXm7HEUbXJJ9rQx4WSLiYfAXEFkL9q0wR444nVZHJVItlCRZJKm4cMN+X6t881fhcTDkC7Nk6MG18OWtUOSjQwtERPzc3vRcVuxKx2aDPq2VJJXL/tXgLISoRIirV+6H1fbFMuBlqdkIbpgI9lBY/z3MHml1RCLVQkmSRVLiIwDYm5FrcSRVIL4+DJ4EwRGweRb88IhKg4uIeKEZxVXtzmtQwz2iQU5j7wmLyNps5X6YX/QkudS/EAa8bd7+9XVY+pG18YhUAyVJFnElSfvSfbTyzV/V7QDXvg/YYOk4WPim1RGJiMhfHK9qV8fiSHyIq2hDOecjudT21bWSTuacQdD1/8zb00bAplnWxiPiYUqSLFKn+M0z11FERq7D4miqSKsroM+z5u2Z/4a1Uy0NR0REjtubnssf248C0Ffzkcpvb8Uq27kk+/JaSSfT7XE4e5A5/PCLW2DPUqsjEvEYJUkWCQ+xUzMqFIC96X70BnrhPdDxDvP213fAtvnWxiMiIgB8t3IvAOc3qukezSCnkZcJhzeZtytQtAGOXwz1+bnHJ7LZzGF3jbubpcEnDIIjW6yOSsQjlCRZqI67K94P5iW52Gxw2YvQ8gooKoBJg81JryIiYqmpK8wk6aq2dS2OxIfsWwkYEJcK0YkVemhtf+xJAggOhes/geRzIOcwfHotZB2yOiqRKqckyUJ14lzFG/zsDTTIDtd+AA06Q36m+Qaats3qqEREAtbGA8f4c18mIXabSn9XRCWH2sHxBWWPZBeQX1hUlVFZLywGhnwJ8Q3g6DaYOBDys6yOSqRKKUmyUN344p6kdD/qSXIJCTdLhtZuA1kH4NNrdKVJRMQi3xb3InVtnkiN4qHeUg6VLNoAUCMyhNBg82PWwUwfXhPxZGJqw01fm2so7V1uzlEq8pM51iIoSbJUHVcZcH9MksBchO6mr8wS4WlbYcJ1kH/M6qhERAKKYRh8s3IPAP011K5iTiz/XUE2m809rN5v/88nNDUXmw2JhC2z4Zt7tdis+A0lSRZyv3n623C7E8Ukw81TITLBXK170hAo9MMraiIiXmrZznR2peUSGWqnV6vaVofjO7IPQ/pO83adcyt1iLrFF0P3+GuSBFDvPBg4Hmx2WPW51koUv6EkyULutZL8qXBDWWo1gSGTITQatv0CXw5Xl7yISDX5ZoXZi9SndTIRoXaLo/Ehe1eY32s1NUdGVII7STrq5//nm/eBa/4L2GDJBzDrKSVK4vOUJFnIlSTtz8jD6fTzN5O67eGGCWAPg/Xfw5S/gdPPJrKKiHiZ/MIivi0u/T2gbYrF0fiYMxhq51K3RgD0JLmcfR1cOca8/evrMP9lS8MROVNKkixUOyaMIBs4igwOZwfAELTG3eD6TyEoBNZ8Bd/er7HLIiIeNGvdQdJzHCTHhnNJs4qVsA54Z1C0wSUghtudqMMw6F28qPzPo2HRu5aGI3ImlCRZKNgeRFKMq8KdH89LOlHz3nDdB+bY5RUTNHZZRMSDJi/dBcC1HepiD7JZHI0PMYyq7Uny9+F2J7roPuj2uHl7xv/B8k+tjUekkpQkWaxOvB8uKHs6Zw2Aq9/FPXb5pyeUKImIVLF9GbnM22guvXBdh1SLo/ExmXvN5Stsdkg+u9KHqRcfCZg9SUYg/Z/r+n/Q6T7z9rf3w6rJ1sYjUglKkiyWEufqig+QniSXcwZB/zfM27+9ZXbLB9I/EBERD/t62R6cBpzfsCaNEqKsDse3uHqRklpBaGSlD5McF47NBvmFTg5nFVRRcD7AZoPeo83hd4YTptwJq76wOiqRClGSZLF6Nc0kaffRHIsjsUD7W+Cy/5i3578MPz+jRElEpAoYhsHkJeZQu4Hn1bM4Gh+0d7n5PaXdGR0mNDiI2sXD6gNmXpKLzQaXvwbthxYnSn+DlZ9bHZVIuSlJslhqDfMK1a60AEySAC64E/o8b96e/wrMfFKJkojIGfpj+1G2H8khMtROv7PrWB2O76mCog0uATkvySUoCK4Yc7xHaepdSpTEZyhJslj9mmaStDNQkySATvcc71Fa+Ab8+E8lSiIiZ2DSH+YiqJefXYeosGCLo/ExhnFCT1IVJEnuCncB+n8+KMjsUXIPvfsbrJxkdVQip6UkyWKuJGlXWoBN6vyrC+6EK14zby96B6Y/pkRJRKQS0rIL+H7VPgAGX1Df4mh8UNpWyEs31/Wr3fqMD+fqSdodiD1JLu5E6VbAgCl3wYqJVkclckpKkiyWEh+BzQa5jqLAmtRZlvOGQ/83ARv8/l/4/mGtoyQiUkGTl+yioNBJm7qxtE2Ntzoc3+MaalfnHLCHnPHh3D1JgZwkQXGi9Kr5vx4Dpt4Dv//P6qhETkpJksVCg4PcFe4CesidS/tb4Kp3ABssHQff3AtFhVZHJSLiE5xOgwmLzaF2N1/YAJtNayNVWBWsj3Qi95ykQCvcUJagIOj3Cpz/N8Aw10qc/4rVUYmUSUmSF6hXI4Ar3JWl7WC45r/m+hQrJ8IXt4AjwEqki4hUwrxNh9iZlkNMeDBXnptidTi+ac9S83sVFG0ASD1huF1AD6t3CQqCy16ELo+aP89+GmY+pSH24nWUJHkBd/GGI0qS3M4ZBNd/ao4J3zANPr0W8jKtjkpExKt9umgHANd1qEdkqAo2VFiRA/atMm/X7VAlh6xXIxKbDbLyC0nLDvBh9S42G1z6BPR6xvz51zEwbYSG2ItXUZLkBVTh7iRa9oObv4bQGNixAD66ArIOWR2ViIhX2pOey8/rDwJw04UNLI7GRx38EwpzISwWajapkkOGh9ipE2uulbRdF0NL6vwAXPk6YIMlH5qLzhY5rI5KBFCS5BVSlSSdXMOLYdj3EJkA+1bCuL6QvtPqqEREvM7HC7fjNOCiJrVokhhtdTi+yT0fqZ05LKyKNKgVBcCOI9lVdky/0WEYXPcBBAXD6snw2Q2Qn2V1VCJKkryBK0kK6PKgp5LSFob/CHGpcGQzfNAHDqy1OioREa+RlV/IxN/NC0i3XdzI4mh8WBXPR3JpmGD+n1dP0km0uRZu+AyCI2DzLBjfD44dsDoqCXBKkryAa7jd3oxcCgo1HrdMCU3NRCmhBRzbCx/2hS1zrI5KRMQrfPHHLo7lFdI4IYruLZKsDsd37am6RWRPpJ6kcmjeG4ZNOz5y5IOecHiT1VFJAFOS5AUSokOJCLFjGCoRekpxdWH4DGjQGfIzYcJ1sPxTq6MSEbFUkdPgw1+3ATD84kYEBansd6UU5MDBdebtKira4NKwlnqSyqVeB7jtJ6jZ2Bxa/0Ev2LnI6qgkQClJ8gI2m83dm7RdV5lOLbIm3DwFzh4IzkJzHaWfn1XpUBEJWD+u3c/uo7nUiAzh2vb1rA7Hd+1fBUYRRNeG2Kotn66epAqo1QRumwl1z4Pco/BRf1j3jdVRSQBSkuQlGieab6BbD+kN9LSCw+Dq/8Ilj5g/z3sJpvwNilRaVUQCz/vztwJmRbuIULvF0fgw13yklPZmieoq1KC4Jyk9x0F6jv5XnVZUAgz9Dlr0g6J8+GIoLHhNF0SlWnl1kjRy5EhsNluJr5YtW1odlkccT5JU0aVcgoKgx7/hyjfMRWdXfY594rWEOrSWkogEjiXb01i2M51QexA3d1LZ7zOyp7iyXRUPtQOIDA0mKSYM0JC7cguNNNdL7HgHYMCskTDlLi0uL9XGq5MkgNatW7Nv3z7314IFC6wOySMaJ5jlWtWTVEEdhsKQyRAaQ9DO3+i6caQq34lIwHjz580AXNO+Lkkx4RZH4+Pcle3aeeTwDTXkruKC7HD5y9Dv5eILopPMNRNV+U6qgdcnScHBwSQnJ7u/EhISrA7JI9w9SYfVk1RhTXvA7bMwajQisuAwwR/1g3XfWh2ViIhHrd6dwS8bDxFkg7u7Vc3CpwErJw2OmsUvqrqynYtryN32w+pJqrDz74CbvoLwONj9B/zvUti3yuqoxM8FWx3A6WzatImUlBTCw8Pp1KkTzz//PPXr1z/p/vn5+eTn57t/zsw0h185HA4cDmtXcXadv6w46seb3fAHMvNJO5ZLTLjX/2q8S40mOG6aRtb4gSQdWwtf3EzRJY/ivORRsHn9tQCfcKrXr5w5ta9n+WP7vjF7IwBXnlOHlNhQS5+br7evbftvBANGzSYUhsSAB55Hag2zp2/roWOVaidfb+MzVv9iGPYjwV8MwZa2BePDPhT1fxuj5ZVVcviAb18P86b2LW8MNsPw3llw06dPJysrixYtWrBv3z5GjRrFnj17WLNmDTExMWU+ZuTIkYwaNarU9okTJxIZGenpkM/Iv5bYyXLY+PvZhdTXYumVYjOKaL1nEk0O/QjA3rjzWNbgTorsGoYiIv5jbw68uDIYGwb/OLeIZO/+9+b1Wu2dTPMD37Gz5iUsb3CHR86x4oiNcRvt1I8y+Ps5RR45RyAIKczmvO1vk3RsDQCbki7nz5TrMGwqWiLlk5OTw+DBg8nIyCA2Nvak+3l1kvRX6enpNGjQgFdffZXbbrutzH3K6klKTU3l8OHDp2yI6uBwOJg5cya9evUiJCSk1P03vv87S3ak8/J1ZzPg3DoWROjbTmzf0HWTsU9/BFtRAUZCcwqvGQeJLawO0aed7vUrZ0bt61n+1r4Pf7GK71fvp2/r2rx5w7lWh+Pz7Wv/dABBO36lsN9rGO1u9sg5Nh/M4rI3FxIZamfFE5diq2AFPV9v4yrlLCRo9kjsv79r/tjgYoqu/h9EJVb6kGpfz/Km9s3MzCQhIeG0SZJPjemKj4+nefPmbN68+aT7hIWFERYWVmp7SEiI5b8Ul5PF0jQphiU70tmZlus1sfqikJAQgs8bCrVbwRe3YDu8kZBxvcxKeOcMtDo8n+dNf0v+SO3rWf7QvpsOHOOHNfsBuO/SZl71fHyyfYscsHc5AMENLwIPxd80OY7gIBs5BUUcyimibnxEpY7jk21c5UKg34tQ/wL45j6Cdiwg6IMeMOhjSO14ZkdW+3qUN7Rvec/vU5M1srKy2LJlC3Xq+Gcvi6t4w5bDqnxTJVLPh7/Nh0ZdwZEDX98O0/4Ohfmnf6yIiJd65aeNOA3o07o2berGWR2O7zuwxvwfER4HCc09dpoQexCNEsz/85sOHPPYeQJKm2vgjp+hVjM4thfGXQZ/vK/1lKRKeHWS9Mgjj/DLL7+wfft2Fi5cyNVXX43dbufGG2+0OjSPUBlwD4hOhJunQJdHzZ//eB8+7AvpO62NS0SkElbuSmfG2v3YbPD33hpCXCV2Lja/1zvfXIPPg5rVNv/Pbz6oSrZVJqmlmSi16g9Oh3kx9KvbIU/rJsqZ8eokaffu3dx44420aNGCQYMGUatWLRYtWkRiYuXHnHqzJknmm+e2w1k4nboKUmWC7HDpEzDkS4ioAXuXwbuXqEy4iPicl3/aAMDVbevSvHbZBYykgnYVJ0mpF3j8VE2TzN/ZpgNKkqpUeKw51K7XM+Z6Smu+hPcuOb72lUgleHWSNGnSJPbu3Ut+fj67d+9m0qRJNGniv2tBpNaIICw4iDyHk51pWkehyjXrBX+bZ66BkZcOX9wM3z0IBWprEfF+v205wvxNhwkOsvFQT88NCws4u343v6ee7/FTNSu+GLrpoIbbVTmbDTo/AMNnQFx9OLodPugNv74BTqfV0YkP8uokKdAE24PcVwbX71c3sUfE14fhP0LnhwAbLB0P/+0K+1dbHJiIyMkZhsFLP64H4IbzU6lfSzW/q0TGbsjcba6nV7eDx0/nGm636WAWPlRc2Lekng93zYezrgJnIcz8N0y4DrIOWh2Z+BglSV6mRbKZJP25T1eZPCY4FHqNglumQnQyHN5ort69aKwme4qIV/pu1T6W70wnIsTO/Zc2szoc/+HqRardBsI8v0Bho4QogmxwLK+Qg8dURMhjIuJh4Hi4YgwEh8OW2TC2M2yaaXFg4kuUJHmZlsVJ0ob9SpI8rnE3uHshtOgHRQUw4x/m1abMfVZHJiLiluco4sXpZi/S3d2aUDtWi2NXmZ2LzO/VMB8JICzYTsNargp3mpfkUTYbnHcr3DEHEltB9kHzf/x3D0K+2l5OT0mSl2lVx1zUSsPtqklULbhhIlz+inm1afMseOcCWPm5epVExCu8P38re9JzqRMXzh2XNLY6HP+yfYH5vWHnajtl0+J5SRtVBrx61D4L7pwDF9xt/rx0PIy9CHYstDQs8X5KkryMa7jdjrQccgoKLY4mQNhs0PF2uPOX4qIOGTDlTpg0RGOYRcRSBzPzeGfuFgD+cVlLIkLtFkfkR7KPwMG15u0GF1fbaVsWXwxdt08XQ6tNSARc9gIM/Q7iUiF9B4zrBz89AY48q6MTL6UkycskRIeREB2GYcBGdcVXr6SWcNtMuPTfEBQCG6bB2xfAmq+sjkxEAtSLMzaQU1BE29R4+p+bYnU4/mVHcS9SYitzTb1q0jrFTJLW7lWSVO0adTGH2be7CTBg4Ztm8SaVCpcyKEnyQq3qFFe401Wm6mcPhi6PwJ1zIflsyE2DL4fDF7fAsQNWRyciAeS3LUf4atlubDZ46sqzsNlsVofkX1xD7RpdUq2nbVM3DoBNB46RX1hUrecWzDWVBrwNN06CqCQ4tB7e70nQzCewF6mYhhynJMkLtXCXAdd4ZcsktzEne3b9BwQFw7pv4K2OsORDrbcgIh5XUOjkianm0gSDz69Pu/o1LI7ID22bb35vWH1D7QBS4sKJjwyh0GmoeIOVWlwG9yyCc64Hw4n993fpvv5xbFt+tjoy8RJKkryQe7yyuuKtZQ+B7o/DHT9DnbaQnwHfPwzjLoODf1odnYj4sf/N38qWQ9kkRIfyWJ+WVofjf7IOwaHi9/FqnI8EYLPZ3EPu1uzJqNZzy19E1YJr/gtDvsKISyWq4DDBkwbB138z56xJQFOS5IXOqWd2xa/Zm0GRUxXWLFfnXDNR6vsChETBrkXw7iXw82hN+BSRKrfzSA5vzN4EwBOXn0VcZIjFEfkh13ykpNbmB+Vq1jrF/D+veUleollPCu+cz5bEPhjYYNUkeLsjLP9Uo0cCmJIkL9QkMZrIUDs5BUVsPqiueK8QZIcL74Z7F0Pzy8DpgHn/gbGdYOOPVkcnIn7CMAz+NXU1+YVOLmpSiwFtVazBIywaaudyvHiDepK8Rmg0a+oNoWjYDDN5zjkC39wLH/aBvSusjk4soCTJC9mDbJxdPLFz5a50a4ORkuJT4cbPYNDHEJ0MaVth4iCYMAiObLE6OhHxcZ/9vov5mw4TGhzEM1e1UbEGT3HNO2nc1ZLTu5KkP/cd04gRL2PU7WAWb+r1DIRGw+7f4b/d4PsRkJNmdXhSjZQkeam2qfEArNydbmkcUgabDc4aAPf9ARfdbxZ22PQjvHMhzBqplbxFpFJ2peXw7LR1ADzWpwVNEqMtjshPHdkCR7eZ792NulgSQqOEaCJC7OQ6ith2WP8zvE5wKHR+wPw/3+Y6wIAlH8CbHczFaDUELyAoSfJS5ypJ8n7hsdB7NNz9GzTpAUUFsOA1eOs8WPUFGLo6KCLl43QaPDJ5JdkFRZzfsCa3dm5kdUj+y9WLVL8ThMVYEsKJI0aW7Uy3JAYph9gUuO4DGPq9uZ5Wbhp89yD8r/vxEvLit5QkeSlX8Yb1+46R59A6Cl4tsTnc9BXc8BnUaAjH9sHXd8D7PWD7r1ZHJyI+4KPftrN4WxoRIXb+M/Ac7EEaZucxm2eZ35tcamkY7RuYZd2X7ThqaRxSDo0ugbvmQ5/nISwW9q2A8ZfDZzfC4U1WRyceoiTJS9WNjyAhOpRCp8E6LSrr/Ww2aNkP7lkMlz5hVsHbsxTG9zPfRA9tsDpCEfFSG/Yf44Xp6wH4Z7+WNKgVZXFEfqwwH7bNM2837WlpKB2Kk6SlSpJ8gz0EOt0D9y+DjreDzQ4bfoC3L4Bpj0D2YasjlCqmJMlL2Ww2zq0XD8AKdcX7jpBw6PIoPLAczht+/E30nQvNLvpj+62OUES8SE5BIfdOXEZ+oZOuzRMZckEDq0Pybzt/A0cORNeG5LMtDaV9/XgANh3MIiPHYWksUgHRiXD5K+ZCtM0vA6MI/vgfvNEO5r8KBTlWRyhVREmSF2tX/Aa6ZIeqqficmNpwxWvmm2jLK8BwmpM932gHs5+BXF05FBF46pu1bD6YRe3YMF4ddC5BGmbnWRtmmN+b9DBHAFioVnQYjRLMXsNlu/Q/weckNofBk2Dod+Z6ivmZMHsUvH4uLH7P7LUUn6YkyYtd2Nhc4G7x1jQMFQHwTYnN4YYJcOsMqNfRvII5/2UYcw7MfQHytEaGSKD6etluJi/dTZANXr+hHbWiw6wOyb8ZBqyfZt5uebm1sRRrX1/zknxeoy5wx1y4+j2Irw/ZB2H6Y/BGe1j6ERSpl9BXKUnyYufUiyc8JIgj2QVaVNbXNegEt82E6z81F6nLz4S5z8OYs81FafOPWR2hiFSjjQeO8cTUNQA82KO5+6KYeND+1ZCxE4IjLC/a4KJ5SX4iKAjOvQHuW2qOIolJgczd8N0D8FZHWPk5OFWEy9coSfJiocFB7qtMi7YesTgaOWM2G7S6Eu5aANeNg4QWZk/Sz6PNnqUFY7TGkkgASM8p4I6Pl5BTUMRFTWpx36VNrQ4pMLh6kZr2gNBIa2Mp5kqSVuxKp6BQa+/4vOBQcz7yA8vNSnhRieaaXFPuNAs8rJioniUfoiTJy7muLi7apnlJfiMoCNpcA/f8Bte8D7WammsvzHoKXmsNc56DbCXFIv6osMjJfROXs+NIDqk1I3h7cHuV+64uXjbUDqBZUjS1okLJKShixa50q8ORqhISblbCe2AF9HgKwuPhyCaYerc5DO/3/4Ejz+oo5TSUJHm5CxrVBDQvyS8F2eGcgWbZ8KvehZpNIC8dfnkRxrSB6f+AjN1WRykiVej56etZsPkwkaF2/nfLedSICrU6pMCQtg0OrDYrjjbva3U0bkFBNi5qmgDAgs0qIe13wqLhkhHw0GroOQqikswhnz88Aq+fA7++oeH2XkxJkpc7NzWesOAgDmfls+VQttXhiCfYg6HtjXDfHzDwI7NKjiMHFo+F19vC1Hvh0EaroxSRMzRx8U4+WLANgFcGnkvL5FiLIwoga740vze6BCJrWhvLX1zc1BwxsmDTIYsjEY8Jj4WLH4KHVkG/lyEuFbIOwMx/m3OTZz+jJUK8kJIkLxceYnePWZ63UW+gfi3IDq2vgjt/gZu+hoaXgNMBKz6FtzvChIGw5WezQpOI+JRZ6w7wxNTVADzUsxmXnV3H4ogCiGHAqsnm7bMHWRtLGS5ulgjAyt0ZZOZpvopfC4mA8+8wF6Qd8HbxcPujZtXb19rAlLtg3yqro5RiSpJ8QPcWSQDM2XDQ4kikWths5sTiYd/DbbPMdZawwaaf4JOrzYVpl47XgnUiPmLZzqPc99kynAYMOq8eD/ZoZnVIgWX/Kji8Aexh0OoKq6MppW58BI0SoihyGizaovmoASE4FNrdBPf+DoM+gfqdzIuiKz+D9y6B8VfAhungVDEPKylJ8gHdW5pJ0uKtaWTnF1ocjVSr1I7mOkv3L4UL7oLQaDi0Hr57EF47C2aNgow9VkcpIiex5VAWt43/gzyHk24tEnn26rOxWbyIacBZXdyL1KIvhMdZG8tJXKx5SYEpyA5n9YfhM+D2n6HNtea8ue3z4bMbzFEki96F3HSrIw1ISpJ8QJPEKOrXjKSgyKk30EBVqwlc9iKMWGeWFY1vYHbRL3jVHM88aQhsmqV1GES8yPbD2Qz+3yKO5jg4p14cbw9uT4hd/3arVVEhrC6ej3T2QGtjOYVLmplJ0uw/D6pIU6Cq1wGu+9Cct3TRAxAWB0c2w4z/g1dawjf3wp5lVkcZUPRu7QNsNhuXFvcmzVmvIXcBLTyuuKzocrh+AjS4GIwiWP89TLjWLPQw7z+aACpisV1pOQz+3yIOZObTLCmaccM6EhUWbHVYgWfTT3BsH0TWgma9rY7mpC5plkh4SBB70nNZuzfT6nDESnH1oPcz5kXRfi9D0llQmAvLP4X/dYf/doNln2jIfTVQkuQjXEPufl5/EKdTV5kCXpDdHFt/6zS4Z5E5FC88ziwt+vNoc72lz2+CzbM1plmkmu1Nz2Xw+4vYm5FH48QoJtxxAbWiw6wOKzAtHWd+bzsYgr33dxARaqdrc7OAw09rdZFLMMuHn38H3L0Qhv9oFh2xh8Le5fDtffBqS5j2iNm7pN5Hj1CS5CMuaFSTqFA7B4/ls2znUavDEW+S1Mocivf3DeZ6S6kXgLMQ/vwOPr2muLzo03B4k9WRivi97YezGfTeb+xKy6VhrUg+u+NCkmLCrQ4rMKXvhE0zzdsdbrU2lnLo2yYZgBlKkuRENhvUvxCu/R+M+NNcb6lGQ8jLgD/+Z/Yujb0IFr4JWRptVJWUJPmI8BA7vVubb6DfrdxrcTTilUIizPWWbvvJvPJ0/p3mmObM3TD/FXjrPPhfD/jjfchJszpaEb/z575Mrnv3N3YfNROkiXdcSO1YJUiWWfYxYECjrua8Ti93aYvaBAfZ2Hggi62HsqwOR7xRVIK53tL9y+HmKdDmOggOh4Pr4KcnzLlLE28wL5IWFlgdrc9TkuRD+p+bAsC01fsoLNIQKjmF2q2h33/gkY1w3ThzLL7NDnuWwLS/wyst4PObYf00KMy3OloRn7d0x1Guf+83Dmfl06pOLJPvuoiU+AirwwpcBTnwxwfm7fOGWxtLOcVFhtCpibmw7PQ16k2SUwgKgiaXwnUfmKNIrngN6nU05yhvnG4Ot3+luVkJd9t8FXWqJCVJPuTiZgnUiAzhcFYBi7aqJ0DKISQc2lwDQyab3fS9n4XabaCoAP78FiYNhv80NRew2/iTrjyJVMK0VfsY/L9FZOYV0qFBDSbdeSGJMd47/yUgrJgAuWnmsKSW3rc20slceY55MfSrZbtV5U7KJyLevBBw+yxz3aXOD0F0slkBd+l4+OgKc57yjMdh91LNX6oAJUk+JMQe5F6l/ZsVWhtHKiimNlx0H9z9K9y1AC68F2LqQH6muYDdxIHwcjP45j7Y8rNZOldETsowDN76eRP3TlxGfqGTS1sm8clt5xMXEWJ1aIGtqNCcnwHQ6T6w+05VwX7n1CEixM7WQ9ks35VudTjiaxJbQK9RZmW8W76BdjebRZ2O7YNF78D7l8Ibbc15yvtWKWE6DSVJPmbACUPujuU5LI5GfFby2dD3OXh4Hdw6HTreAVFJkJcOyz+BT642u+q/fQA2/giOPKsjFvEq+YVF/H3ySl7+aSMAt3ZuyP9uOY/IUN/5QO631k6B9B1m2e+2Q6yOpkKiw4K5rLiAw5dLd1scjfisIDs07gYD3oJHNsONk8z5SyGRcHS7OU/5vUvg9XNhxj9hx0INySuD3s19zPmNatIkMYoth7KZunwPN3dqaHVI4suCgqDBRebXZS/C9gWw9mtY9y3kHIFlH5lfIVHYm1xKvdwUyO0EIUlWRy5imV1pOdwzYRmr92RgD7Ixsn9rbr6wgdVhCUCRA+Y+Z96+4G4IjbQ2nkq4rkM9vl6+h+9W7uXJK84iPMRudUjiy4JDocVl5ldBNmycwf+3d+fxUZX3/sA/Z9Zksi9kgyzskVUWicG6FJBVhEpFuZaKVXq14JXSey96bxWt90ot/XmtlAv+WgH7s7VI3aqilF3BsIZ9CRBCSMiekHWyzMx5fn88M5PJvshkJpPP+/V6PNtzzjx5fDjnfM/yHJz9CLiyS15MOLRepoB+Mk/yXGDQvV7dZX5PYZDUyyiKgh/dmYhXPjuP9w5dx4/uTISiKJ4uFvkCjVbuGAfdKz9gl/U1kLEduLgdqMqD5uJnmABA/M8fZVCV/AAwfKZ85p+oj9h9oRArPziFiloLQk16vPXoONxj/74NeYET7wFlVwFTJHDn054uTbfcOSgC/UP9caO8Fn8/lYeFE+M9XSTyFYYAYNQCmRpq5LcUL34uA6eaYtkjZPqfAEMQMGSq7PRpyDT5uH4fxCCpF3po/AD85qsMZBRW4Vj2TdyRFO7pIpGv0erlDnLIVBkw5Z2A7fznqDm+FcF1ucC1b2T6ahUQMUTuRIfcDyTdJbsiJ/IxDVYVb+y8hI37MwEAY+ND8b+PjUd/9mDnPRrMwP7X5fg9/wYYgzxbnm7SaBQsTk3Er7+8iE0HsvDwhAG8GEq3niEAGPGgTDaLPKZf+Fz2eltdAJz/RCYAiB0rA6ah04H+E+RF1T6AQVIvFOKvx4Nj47D1WA7++M1VBknkXooC9B8PNWo09prHYnbqCOgz/yHvMF1PA0qvyHR4o/xeQ+Jd9qBpGhA5VK5P1ItlFFRhxdaTuJBfCQBYMjkJ/zH7Nhh0fK3Xq3zzf+QL6iEJwETv/3hsexbdkYC3dl/GxYIqHLhSgruH8m4luZFWL7sUHzzFfmE0Xb6PfGUnkHcCyD8l09drAf8wYPBUYOj98r2noBhPl95tGCT1Uk/dPRAfHM/BjnOFyCiowvCY3nnFjHqhsCQgdZlMdRXysbzLO+Vt+8pcIHO3TDtekCcrg+4Bku4BBt4NBMd5uvREnWZTBTYdyMLaHRlosKkIM+mx5qHRmDkq1tNFo+aKLwEHfyfHZ77W69+nCDHpsXBiPLZ8ew1/+CaLQRL1HI0GGDBRpin/CVQXyfeXLv8DuLJHdi1+9m8yAUDkcPmY/sB7gaTvyS7JfQSDpF5qaHQQZo2KwfYzBfj93itYt2icp4tEfZFfCHDbXJmEAIoz5M70yi4g+yBQcV2+I3DiPZk/Yggw8B6Zku6WXw8n8kJnb1TgPz8+g1O5FQCAKclR+PWC0YgK8vNwyagFVQW+WAmoFmDojF71XaT2/OSugfhT2jV8fakYp3LKMSImwNNFor4oMAq4/Z9kslmB3KMyYMrcLbsRL8mQ6cj/BRQNEHu7PWi6B0hI7dWP4DNI6sWWf38otp8pwOen8/Dc1CEYEsW7SeRBigJEJcs0ebl8KTT7W3mn6do38la949G8Y5vkOlEj5ZWnhBQg/k4gpL9n/wbq82rqrXhj5yVsPpgFVQBBRh3+Y85tePSOeL4X4q0Ob5T7GJ0/MPs3PvOIb0KECT8YNwAfpudi7Y4MbH58vKeLRH2dVgckpso0bTVgLpP/9q7ul8f60svyUb28dODA/wBag3yHKXEylP4p0NlqPf0XdAmDpF5sRFww7h8RjZ3nC/Ha9ovYtOQOTxeJqJEhQD6zPPR+OV1b3jRoKjwLFJ2T6cjbMk9Igj1gSgES7gSiRvSZF0TJs1RV4O+n8vD6VxeRXyG/CzZnTCxeemAEooN598hrFZ4Ddr0sx2f8l8/1trli2lD8/dQNHLhSgm8zSz1dHKKmTOHAiHkyAUDFDXmMz9ovA6eqPPnu8vU06AB8zy8ewAJPlrhLGCT1cs/PSsbei0XYc7EI+zKKcN9wfr+GvJR/KJA8WyYAqCmRwVJ2GpBzCCg4Ix/PO3MdOLNN5jEGy+ei4+8E+o8H4sYDAREe+xPINx26Wor//uICztyQj9bFh/vj1XmjuD/1dnUVwLYlgK0eGDYTmPikp0t0y8WHm/BYSiK2fHsNr++4hKf4OS7yZiH9gdsXySQEcDMLuHYQyP4WIvsgynSD0Zu+XMYgqZcb3C8QSyYn4Y8HsvDq5+eROjgCRh2vvFMvEBAJjPyBTABQXwXkHgNyDgPXD8nx+kogc49MDqGJjQFT//Gya9Je2tUvedbZGxV4c9cl7LpQBAAINOrwzH2D8eT3BvIDnt5OtQEfPgWUXAKC+wMP/t5nHrNrbvmUIfj4xA2cz6/C1wYFcz1dIKLOUBQgfJBM4xfDarHgzOd/xwBPl6sLGCT5gGenDsUnJ28gs7gGv9t1Gf8+M9nTRSLqOmMQMPj7MgHyJKjwnAyaco7IZ5xLr8gvhJdnA+c+tq+oAP2Gy6ApbhwQMxqIHgn4BXvsTyHvdjq3HG/tvuwMjrQaBYsmxWPFtGGIDOzdvaL1CUIAX66SL4/r/IBH/wwE+m7vb5GBRrwwKxnPf3QG23M0+Hl5LRL76T1dLKIuE5reFXb0rtJSq0L89fiv+aPw9Hvp2Lg/E1Nvi8aExDBPF4vou9FogdgxMk1aKufVlgP5J4Eb9hdDb5yQ3Y4XX5Tp1F8a1w9LAqJH2YMm+zA0wWevNlP7hBC4XKHgqf+Xjv2XSgAAGgWYOzYOz04ZiiFRgR4uIXWKEMDuXwFH/wBAAeZvkBdHfNzCifH42/EcHMsux6qPzuLPS1Oh1XBfRuRODJJ8xMxRsfjBuP74+MQNrNh6Ap8t/x5CTQZPF4vo1vIPlR+vG3Rf47yqQvmxuxvHgYLT8t2myhvAzWsyXfy8Ma8xRN5lihkF9EtuTHzPyWfVWWz47FQe3jmQhYsFWgAl0CjA/HH9sez7QzC4H4OjXkNVgX/8Eji0Xk4/8AYw6iHPlqmHaDQKXps/Eg+sO4BDWTfx+z1X8Ny0oZ4uFpFPY5DkQ16eOxLHssuQU1aLZ98/gS1PTOKVJvJ9QdHA8JkyOZjLZO95BWeAAvuw+CJQXwFc/1YmV6ZIe8A03CUlA4HRvPPUS10trsa247nYdiwHJdUNAACDRuCHE+Px03uGICmS35zpVaz1wKfLGjt1mbEGmPgTz5aphw2MDMDDg1T8+YoWv9t9CbcnhOLeYb77mCGRpzFI8iEhJj3e/tFELNjwLb65XIJffXYOLz84kt/2oL7HFN740VoHa4N8ybvgjAygSi7JwKn8OmAuAbIPyOTKL0R+TTxiMBA+GIgYZB8OZmcRXsjcYMX2MwX44GgOjlwrc86PDfHDj1LiEVZ2AQ/PHQG9nu9z9Co3r8le7PJOABodMO9/gbGPeLpUHjGpn0BdcBw+TM/Dz947jg+eTsXIuBBPF4vIJzFI8jEj4oLx24fHYtlf0vFuWjYC/XT4txnsyIEIOoN8zC5mVNP5DTX2gCnD/m6TfXjzmuxiOPeITM0FRDUGT+EDm44zgOoxdRYb9l8qxhen87H7QiFqGmwA5PtG9w7rh4UT4zFtRDSg2rB9+wUPl5a6RAjgzN+A7b+Q/xb9w4AF7wBDpnq6ZB71q7kjkFdej7SrpXhi81G8/9M7+dgokRswSPJBc8bEosw8Ci9+chbr92bCahNYNTMZGj56R9SSIUC++N385W9LrexNr+QSUHoVKMsEyq4CpZnyzlNNkUzX01pu0z9cdhLhTIku4/EMor6jmnorDlwpwZdn8rHrQhGq663OZQnhJiycOAALJgxAbIi/c75FtXmiqNRdZVnAl/8ue7ADgP4TgYe3yH8/fZxBp8HGxROwcGMaMgqr8Mjbh/CXpSkYFs39CtGtxCDJRy2+MxF1DTb89/YLePvrqyisrMOvF4zhtz+IOkvvL3vEixndclldRWPA5DosywTMpUBtmUz5J1vfdvMgKjgOSkA0wmquARW5QNgAQMtHwhyEEMgqqbF/NLsYR7LK0GBTnctjQ/wwZ3Qs5oyJxe3xoXzEuDerLgL2/wY4vhlQrYDWANzz78Bdz8m7wQRA9mr7l6Up+NE7R3AhvxILNnyLdYvG8QPIRLcQgyQftvSeQQgLMGDVh6fxyck8XCyowvrHxvO2PNF35RfS+t0nQH4UtzzH/j2n6/bkMl57s9UgSgfgHgC49CoARXYaERwrP5QZFAsEx8nx4FggKA4IjJJ3pHw0IMgrr8XhrFIcvlqGbzNLcb3M3GR5QrgJU2+LwgNj4jAuPpR3ynu7ogvAoQ3A6a2AtU7OGzwFmPk60G+YZ8vmpSICjXh/aQqeevcYjmXfxE+2HMUvpg/H0/cOZqdNRLcAgyQf98MJAxAX4od/+esJXCyowtx1B/CL6cPxeGoidFqNp4tH5HuMQUD0CJlaU1cJVOS4BFDXgap8qBU3UFt4BSZrBRTVAlQXyJR3ou3f0vnLj2gGRst3pAJdUkCUnO9YbvDe3txsqkBmcTVO5ZTj6LUyHLpa1iIo0msVpAyMwH3D++H7yVEYFBnAO0a9nbkMOP+pfO/ItdOUuPHAtJeBQfd6rGi9RajJgD8vTcHqT8/hr0dzsHZHBvZcLMLaH47BIF4QJfpOGCT1AZOHRGL7v9yN5/56EmlXS/Hq5+fxUXounp+VjO8NieSJBlFP8gsG/EbK7zW5sFks2LV9O2bPmgl9Q6X81lNVvhxW5gGV+S7z8oCGasBa2xhodUQfIAMmUyRgipA9ALoO/R3T9uQfBmhv/SFCCIHcm7U4c6MCp3LKcTKnHGdvVDg7XHDQKMCo/iG4c1AEUgaGI2VQBAKNPGT1akLId/wy9wBXdgFX9wOqRS5TNEDyHODOnwEJqT57h9QdjDot1jw0GuMTwvCrz8/jePZNzHzzGzw+ORHLvj+E30wk6iYecfqIqGA//PmpFGw9loM12y/gXF4lFr9zBJOSwrFsyhDcPSSSj6sQeQNFY7/70w/A7W3nazDLjiOqi4DqQvuwyGWey3xrLWCpAW7WyF77OssvpGkA5R8q5/mFAH6u4y7JPxQwBgMaLUqr65FRWIVLBVXIKKxGRkElLhdWo8qlowUHf70Wo/uHYFxCKO4cFIGJSWEI8uN7Wb1agxnIPwXkpcuPPV8/DFTmNs0TPRoY/UNg1AJ2yvAdKIqChXfEY/KQCPzHx2fx9aVi/OGbLGw9moMlk5Pwo9RERAX5ebqYRL0Kg6Q+RKNRsGhSAu4fEY31e6/gz4ev48i1MhzZdARJESb8U0oCHhgTh7hQ/443RkSeZTABhiQgLKn9fELIu06OwKm2THYu4Uw3G8cdy2pvynXrKmTC1S4Xrxom1AoTwoQJt8GEOBGAO+CPGuEHs94fpsAQhIWFIzoyEnFRkYjpFwmtHwCjFTDUADYNYAkEdEbeVfBmqg2oKYZSchXxpd9As+eY7MCk5JLszEQ061VQawQSU4HBU4Gh04EofqLiVhoQZsK7T9yB/ZeKsWb7RWQUVuGtPVewcf9VzB4dg/nj+uN7QyL5uD1RJzBI6oMiA41YPXckfnrPILy9/yo+PJ6La6VmvLb9Il7bfhHjEkIxc2QM7hoSiRGxwbzDRNSbKYp8T8oYJL/l1Bk2K1BXDkt1CcqK81FZWojqm0WoqyqDpaYMNnM5lPoKaOor4a9WIxhmhCg1CIYZJqUeABAIMwIVM9DW7qPWnvI6KItGJ9+nMgQBxkA5rjfJ3gd1fo3jzmGzefY8ikaP0JpMoOg84B8k3+fSGWXvaTqj/B0GYzKottS6dDByU7475JiuKW18BLQqXybVCh2A8QDQ/MnPwBig/wSg/3g5jE+RAT65jaIouG94FO4e2g9fnS3AOweuIv16OT45mYdPTuYhMtCA+0dE456h/TB5SCRC/HnHlqg1DJL6sNgQf7z84Ej824zh+PupPHycfgNHs8tw4no5TlwvBwCEmvRIGRiOMQNCMap/CEbFBSMi0OjZghNRt9VZbCitaUBpdT1KqxtQUl2PspoGlNbI8dLqBpTW1KOwsh4l1fUQAgCC7Kn1ICsqyIikiAAkRpgwMEyPwcEqkgKsSAxogJ+tGqgtl3ek6qvkXa36ajl0HW++zGLvuEG1utzR6j4dgHsB4NIrbeRQGgMmrcE+bpB3PlzHdQaX5fZlGh2g0doDLftQo2k2rZWpybROPl6p0cqhg6x0F6L9ZTarfLfHZrEPXaetMjmWWevlY3CWGvvQLD+obDE3Tje/+9MRRQMRGIMSEYrw4anQRiUDkUOBfsNlr4zkEVqNgjljZNf4J3PK8VF6Lj4/nY+S6ga8fyQH7x/JgVajYFRcMG6PD8XtCaEYMyAUSREB7B2PCL0kSFq/fj3Wrl2LgoICjB07FuvWrcOkSZM8XSyfEWDUYdGkBCyalIDCyjrsOFeA/RnFOJxVhnKzBTvOFWLHuUJn/qggI5IiAzAwIkAOI02IC/VHVJAfIgMNvI1P5AZCCNRbVZgbbKipt6LWIofmBhsqay2orLOgstaKCue4BZV19mmX5bWWrp0AG3QaxIb4ISbYD7EhfogN9XdOx4ebkBBuQoA7OlRQbS5BUw3QUNU4bq2VdzuaJLMcWpvPqwMsZgiLGbWVZfDXK1Acy4Tq8oMCsNXLRDKA8w+XHXj4h8kOPhxDZ7f0/WUQFBgNqyrw7fbtmD1zNrR63pnwNrfHh+L2+FC8+MAIHLxSgn0Zxfj6cjGuFtfgVG4FTuVW4N20bADy3/ygyAAMjgrE4H6BiA/zR5z9331siD/8DfzeIvUNXh8kbd26FStXrsTGjRuRkpKCN998EzNmzEBGRgaiovjRtFstOtgPP05Nwo9Tk2CxqTidW4Hj2WU4e6MSZ29U4GpJDYqq6lFUVY8jWWUt1lcU+ThfVJAR/YKMCPXXI8SegpuNmwxa+Ou18NNr5bhBCz+dlo/3kVdSVQGrKmBVVVhVAZtNwKKqsKkCVptAvdWGequKequKhiZDG+otKhpsKuotNvvQvtxlXm29FVdzNPhb8XHUWmQwZG6w2odyXG1+E6GbDFoNIgINiAg0IDzAiMgAg33aiIgAAyID5b/f2BA/hAcYPNMDpkbb2BnELWC1WLBz+3bMnj0besdJvGqTd1Zs9YC1AbDZk7W+2Xi9vBPTYr59qFplwKXa7OP2oao2m7bJ1GTaPmyuRZ0rbS/T6OTHhzV62SOhRm+f1rVcpjXKx930AfahqfERRsfQGAgYArv2+KGjlzryanqtBvcNj3J+dDb3phnp18tx8no5TubcxLm8StRbVVwsqMLFgqpWtxFq0iMqyIgwk0GmAAPCTHqEBxgQ4q9HoFEHk1GHAIMWJoMOAcbGob9eyx51qdfw+iDpjTfewNKlS/HEE08AADZu3IgvvvgCmzZtwvPPP+/h0vk2vVaDCYlhmJAY5pxXVWdBZnENrpXUIKukBtdKa3Ct1IzCijoUV9fDpgoUV9WjuKr7V2ONOg387QGUXquBXqvYhxronOON8xzjGgUouKFB2t/PQ6/VQqPIzio0igKtRoGiAFpFTre3TFHkYwoaRYFGozhPTRQFcEw59vFKk/HGmY3rKC3zueRvfqxQlPZ/z/GkjbA/fiNE44M4wuUxHNd8zvFW8grnf5rlhes6cspqteFsgYKbR3Kg1Wic+Rq3L5qs17hcTqhCwKbKoRACqpDjqpB5bGrjuOqyXAjYl7WyXG2ZV7Uvt6ktt2W1NQY5clzAapOBjkVV7YGPLIvF1hgAWVX1lgUo7dMAZaUd5vLTa2Ay6GAyaBFg0CHYX4dgP3nxIdhP57wgIec1LnNcoAj20/FEBZCBmMEEgO/IUN80IMyEAWEmPDhWPhZpUwVu3KzFleIqXCmqRmZRDfIqapFXXov8ijqYG2woN1tQbu5eUKwo8hiv12pg1Glg0Gpg0Mmkd4zbh458Wo08Tmvtx2StokCrtQ/tx2qd1nE8h32+BloNnPkdx3bHMVZRHMdlxTluU1WcK1BQcTQHOq3OJY/9WOy6jmO+0vRY3nT7TfexAi2P0Y3Lmk23eKy2+fLm64s2l3f1txznBQKOY27j+YHjmC5E4zHfcdx1/JYjr+oyDshziNISBbPb/cu8i1cHSQ0NDTh+/DheeOEF5zyNRoNp06YhLS2t1XXq6+tRX994gl5ZWQkAsFgssFg8e6XL8fueLsd34acFRsYEYGRMyw9T2lSBspoG552mkup6VNRamz72U2dBRa0VVXXysZ86i80+bHzsxXE1vhzdqScN0opyO85G3aTFtqwLni6E11AUQKdRoNMoMOq0zoO9wX4CYNQ3jjsO+o5xg04Lg7ZxPa0ikJ15CRPGjEKgv8F+FVbeYQ0w6ODvmNZrv/P7AlZryy64fZ0v7H+9GevX/XqqjmOD9YgNDsfdg8ObzBdCoLLOivyKOpRUN6Dc3IDyWgtummUqNzegotZifyTY1uRuuOM7aEIAdRYVdRYVrd+n8jQe49zptlDFK/YRnS2DVwdJJSUlsNlsiI6ObjI/OjoaFy9ebHWdNWvW4JVXWr6Y+49//AMmk3dcLdy5c6eni9AjAuwpDgC0AALtqRWqAKwq0OBINsCiAlYB2FTAJhQ57kiqy7h9Xce4vHqhyCsZsF/ZcB1H02nX+Wqz5a53D9q7GtP8DkqL5a2s29Fyx/2o5ssabwC45mq+zOVuVhvL21qmdLDcdZnSLGN762rQePVO0+QKXfvzNY7fUkTL5Y48zaabb8vx21p73pZD0c6ylkPH+Hd+MlQF0GBPABJiABSdgQqgyp7o1uor+19PYf26n7fUsQZAuD1BQeNBvxWqkMf0epdju9VlaBNKk+nG+bA/CWBPgPMuhQrFOd/1mC0EYGuSr+mxH3A5Ztv/43pMbi1Pa09ZOJe1up48B2l5w77pAb0zh5CObvq382Dud9qm41jqOu0cbzYNND0uw3XcZVmMSXhF+zWbzZ3K59VBUne88MILWLlypXO6srIS8fHxmD59OoKDgz1YMhm57ty5E/fff3/jM/F0y7B+3Yv1616sX/di/boX69f9WMfuxfp1L2+qX8dTZh3x6iApMjISWq0WhYWFTeYXFhYiJiam1XWMRiOMxpZdVOv1eo//T3HwprL4Itave7F+3Yv1616sX/di/bof69i9WL/u5Q3129nf9+q+mg0GAyZMmIDdu3c756mqit27dyM1NdWDJSMiIiIiIl/l1XeSAGDlypV4/PHHMXHiREyaNAlvvvkmampqnL3dERERERER3UpeHyQ98sgjKC4uxksvvYSCggLcfvvt+Oqrr1p05kBERERERHQreH2QBADLly/H8uXLPV0MIiIiIiLqA7z6nSQiIiIiIqKexiCJiIiIiIjIBYMkIiIiIiIiFwySiIiIiIiIXDBIIiIiIiIicsEgiYiIiIiIyAWDJCIiIiIiIhcMkoiIiIiIiFwwSCIiIiIiInLBIImIiIiIiMgFgyQiIiIiIiIXDJKIiIiIiIhcMEgiIiIiIiJyofN0AdxNCAEAqKys9HBJAIvFArPZjMrKSuj1ek8Xx+ewft2L9eterF/3Yv26F+vX/VjH7sX6dS9vql9HTOCIEdri80FSVVUVACA+Pt7DJSEiIiIiIm9QVVWFkJCQNpcroqMwqpdTVRV5eXkICgqCoigeLUtlZSXi4+ORk5OD4OBgj5bFF7F+3Yv1616sX/di/boX69f9WMfuxfp1L2+qXyEEqqqqEBcXB42m7TePfP5OkkajwYABAzxdjCaCg4M93kB8GevXvVi/7sX6dS/Wr3uxft2PdexerF/38pb6be8OkgM7biAiIiIiInLBIImIiIiIiMgFg6QeZDQasXr1ahiNRk8XxSexft2L9eterF/3Yv26F+vX/VjH7sX6da/eWL8+33EDERERERFRV/BOEhERERERkQsGSURERERERC4YJBEREREREblgkEREREREROSCQdIttn79eiQlJcHPzw8pKSk4cuRIu/m3bduG5ORk+Pn5YfTo0di+fXsPlbR3WbNmDe644w4EBQUhKioK8+fPR0ZGRrvrbNmyBYqiNEl+fn49VOLe5eWXX25RV8nJye2uw7bbNUlJSS3qWFEULFu2rNX8bL/t+/rrrzF37lzExcVBURR88sknTZYLIfDSSy8hNjYW/v7+mDZtGi5fvtzhdru6D/dV7dWvxWLBqlWrMHr0aAQEBCAuLg4//vGPkZeX1+42u7Of8VUdtd8lS5a0qKuZM2d2uF22X6mj+m1tX6woCtauXdvmNtl+pc6cj9XV1WHZsmWIiIhAYGAgFixYgMLCwna32919tjsxSLqFtm7dipUrV2L16tVIT0/H2LFjMWPGDBQVFbWa/9tvv8WiRYvw5JNP4sSJE5g/fz7mz5+Ps2fP9nDJvd/+/fuxbNkyHDp0CDt37oTFYsH06dNRU1PT7nrBwcHIz893puzs7B4qce8zcuTIJnV14MCBNvOy7Xbd0aNHm9Tvzp07AQAPP/xwm+uw/batpqYGY8eOxfr161td/pvf/AZvvfUWNm7ciMOHDyMgIAAzZsxAXV1dm9vs6j7cl7VXv2azGenp6XjxxReRnp6Ojz76CBkZGXjwwQc73G5X9jO+rKP2CwAzZ85sUlfvv/9+u9tk+23UUf261mt+fj42bdoERVGwYMGCdrfL9tu587Gf//zn+Oyzz7Bt2zbs378feXl5eOihh9rdbnf22W4n6JaZNGmSWLZsmXPaZrOJuLg4sWbNmlbzL1y4UMyZM6fJvJSUFPHP//zPbi2nLygqKhIAxP79+9vMs3nzZhESEtJzherFVq9eLcaOHdvp/Gy7391zzz0nBg8eLFRVbXU522/nARAff/yxc1pVVRETEyPWrl3rnFdeXi6MRqN4//3329xOV/fhfUXz+m3NkSNHBACRnZ3dZp6u7mf6itbq9/HHHxfz5s3r0nbYflvXmfY7b948MWXKlHbzsP22rvn5WHl5udDr9WLbtm3OPBcuXBAARFpaWqvb6O4+2914J+kWaWhowPHjxzFt2jTnPI1Gg2nTpiEtLa3VddLS0prkB4AZM2a0mZ8aVVRUAADCw8PbzVddXY3ExETEx8dj3rx5OHfuXE8Ur1e6fPky4uLiMGjQIDz22GO4fv16m3nZdr+bhoYGvPfee/jJT34CRVHazMf22z1ZWVkoKCho0kZDQkKQkpLSZhvtzj6cGlVUVEBRFISGhrabryv7mb5u3759iIqKwvDhw/HMM8+gtLS0zbxsv91XWFiIL774Ak8++WSHedl+W2p+Pnb8+HFYLJYmbTE5ORkJCQlttsXu7LN7AoOkW6SkpAQ2mw3R0dFN5kdHR6OgoKDVdQoKCrqUnyRVVbFixQrcddddGDVqVJv5hg8fjk2bNuHTTz/Fe++9B1VVMXnyZOTm5vZgaXuHlJQUbNmyBV999RU2bNiArKws3H333aiqqmo1P9vud/PJJ5+gvLwcS5YsaTMP22/3OdphV9pod/bhJNXV1WHVqlVYtGgRgoOD28zX1f1MXzZz5kz86U9/wu7du/H6669j//79mDVrFmw2W6v52X67791330VQUFCHj4Ox/bbU2vlYQUEBDAZDiwsmHZ0PO/J0dp2eoPPYLxN107Jly3D27NkOnwVOTU1Famqqc3ry5Mm47bbb8Pbbb+PVV191dzF7lVmzZjnHx4wZg5SUFCQmJuKDDz7o1NU16pp33nkHs2bNQlxcXJt52H6pN7BYLFi4cCGEENiwYUO7ebmf6bxHH33UOT569GiMGTMGgwcPxr59+zB16lQPlsz3bNq0CY899liHHeOw/bbU2fOx3op3km6RyMhIaLXaFr13FBYWIiYmptV1YmJiupSfgOXLl+Pzzz/H3r17MWDAgC6tq9frMW7cOFy5csVNpfMdoaGhGDZsWJt1xbbbfdnZ2di1axeeeuqpLq3H9tt5jnbYlTbanX14X+cIkLKzs7Fz58527yK1pqP9DDUaNGgQIiMj26wrtt/u+eabb5CRkdHl/THA9tvW+VhMTAwaGhpQXl7eJH9H58OOPJ1dpycwSLpFDAYDJkyYgN27dzvnqaqK3bt3N7ka7Co1NbVJfgDYuXNnm/n7MiEEli9fjo8//hh79uzBwIEDu7wNm82GM2fOIDY21g0l9C3V1dXIzMxss67Ydrtv8+bNiIqKwpw5c7q0Httv5w0cOBAxMTFN2mhlZSUOHz7cZhvtzj68L3MESJcvX8auXbsQERHR5W10tJ+hRrm5uSgtLW2zrth+u+edd97BhAkTMHbs2C6v21fbb0fnYxMmTIBer2/SFjMyMnD9+vU222J39tk9wmNdRvigv/71r8JoNIotW7aI8+fPi5/+9KciNDRUFBQUCCGEWLx4sXj++eed+Q8ePCh0Op347W9/Ky5cuCBWr14t9Hq9OHPmjKf+BK/1zDPPiJCQELFv3z6Rn5/vTGaz2Zmnef2+8sorYseOHSIzM1McP35cPProo8LPz0+cO3fOE3+CV/vFL34h9u3bJ7KyssTBgwfFtGnTRGRkpCgqKhJCsO3eKjabTSQkJIhVq1a1WMb22zVVVVXixIkT4sSJEwKAeOONN8SJEyecvav9+te/FqGhoeLTTz8Vp0+fFvPmzRMDBw4UtbW1zm1MmTJFrFu3zjnd0T68L2mvfhsaGsSDDz4oBgwYIE6ePNlkn1xfX+/cRvP67Wg/05e0V79VVVXiX//1X0VaWprIysoSu3btEuPHjxdDhw4VdXV1zm2w/bato/2DEEJUVFQIk8kkNmzY0Oo22H5b15nzsaefflokJCSIPXv2iGPHjonU1FSRmpraZDvDhw8XH330kXO6M/vsnsYg6RZbt26dSEhIEAaDQUyaNEkcOnTIuezee+8Vjz/+eJP8H3zwgRg2bJgwGAxi5MiR4osvvujhEvcOAFpNmzdvduZpXr8rVqxw/r+Ijo4Ws2fPFunp6T1f+F7gkUceEbGxscJgMIj+/fuLRx55RFy5csW5nG331tixY4cAIDIyMlosY/vtmr1797a6T3DUoaqq4sUXXxTR0dHCaDSKqVOntqj3xMREsXr16ibz2tuH9yXt1W9WVlab++S9e/c6t9G8fjvaz/Ql7dWv2WwW06dPF/369RN6vV4kJiaKpUuXtgh22H7b1tH+QQgh3n77beHv7y/Ky8tb3Qbbb+s6cz5WW1srfvazn4mwsDBhMpnED37wA5Gfn99iO67rdGaf3dMUIYRwzz0qIiIiIiKi3ofvJBEREREREblgkEREREREROSCQRIREREREZELBklEREREREQuGCQRERERERG5YJBERERERETkgkESERERERGRCwZJRERERERELhgkERGRT1iyZAnmz5/v6WIQEZEP0Hm6AERERB1RFKXd5atXr8bvfvc7CCF6qEREROTLGCQREZHXy8/Pd45v3boVL730EjIyMpzzAgMDERgY6ImiERGRD+LjdkRE5PViYmKcKSQkBIqiNJkXGBjY4nG7++67D88++yxWrFiBsLAwREdH4w9/+ANqamrwxBNPICgoCEOGDMGXX37Z5LfOnj2LWbNmITAwENHR0Vi8eDFKSkp6+C8mIiJPYpBEREQ+691330VkZCSOHDmCZ599Fs888wwefvhhTJ48Genp6Zg+fToWL14Ms9kMACgvL8eUKVMwbtw4HDt2DF999RUKCwuxcOFCD/8lRETUkxgkERGRzxo7dix++ctfYujQoXjhhRfg5+eHyMhILF26FEOHDsVLL72E0tJSnD59GgDw+9//HuPGjcNrr72G5ORkjBs3Dps2bcLevXtx6dIlD/81RETUU/hOEhER+awxY8Y4x7VaLSIiIjB69GjnvOjoaABAUVERAODUqVPYu3dvq+83ZWZmYtiwYW4uMREReQMGSURE5LP0en2TaUVRmsxz9JqnqioAoLq6GnPnzsXrr7/eYluxsbFuLCkREXkTBklERER248ePx4cffoikpCTodDxEEhH1VXwniYiIyG7ZsmUoKyvDokWLcPToUWRmZmLHjh144oknYLPZPF08IiLqIQySiIiI7OLi4nDw4EHYbDZMnz4do0ePxooVKxAaGgqNhodMIqK+QhH8PDkREREREZETL4sRERERERG5YJBERERERETkgkESERERERGRCwZJRERERERELhgkERERERERuWCQRERERERE5IJBEhERERERkQsGSURERERERC4YJBEREREREblgkEREREREROSCQRIREREREZGL/w/yaLGTQvSwdAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the results\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(t_span, data[:, 0,0], label='Prey (x1)')\n",
    "plt.plot(t_span, data[:,0, 1], label='Predator (x2)')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Population')\n",
    "plt.title('Standard Lotka-Volterra Predator-Prey Model')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's add some stochasticity: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sigma1 = 1\n",
    "sigma2 = 0\n",
    "g_params = torch.tensor([[sigma1, sigma2]])\n",
    "# Create the SDE model\n",
    "sde = LotkaVolterraSDE(a, b, c, d, g_params)\n",
    "\n",
    "\n",
    "t_span = torch.linspace(0, 20, 2000)\n",
    "\n",
    "# Initial condition\n",
    "x0 = torch.tensor([10.0, 10.0]).unsqueeze(0) #[1x2]\n",
    "\n",
    "# Integrate the SDE model\n",
    "data_stochastic = torchsde.sdeint(sde, x0, t_span, method='euler')\n",
    "\n",
    "# Plot the results\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(t_span, data_stochastic[:, 0,0], label='Prey (x1)')\n",
    "plt.plot(t_span, data_stochastic[:,0, 1], label='Predator (x2)')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Population')\n",
    "plt.title('Stochastic Lotka-Volterra Predator-Prey Model')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Training a Neural SDE Using Reference Tracking + FD Loss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now show how might attempt to train a Neural SDE to fit this data using our standard Neuromancer training procedure for Neural ODEs. First we construct a Neural SDE with learnable parameters. Note that to ensure the output size condition of $g$ we pass in batch size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "class LotkaVolterraSDELearnable(BaseSDESystem):\n",
    "    def __init__(self, block, batch_size):\n",
    "        super().__init__()\n",
    "        self.block = block \n",
    "        self.batch_size = batch_size\n",
    "        self.alpha = nn.Parameter(torch.tensor([.10]), requires_grad=True)\n",
    "        self.beta = nn.Parameter(torch.tensor([.10]), requires_grad=True)\n",
    "        self.delta = nn.Parameter(torch.tensor([.10]), requires_grad=True)\n",
    "        self.gamma = nn.Parameter(torch.tensor([.10]), requires_grad=True)\n",
    "        self.g_params = nn.Parameter(torch.rand(self.batch_size, 2), requires_grad=True)\n",
    "\n",
    "\n",
    "    def f(self, t, y):\n",
    "\n",
    "        x1 = y[:, [0]]\n",
    "        x2 = y[:, [-1]]\n",
    "\n",
    "        dx1 = self.alpha*x1 - self.beta*self.block(y)\n",
    "        dx2 = self.delta*self.block(y) - self.gamma*x2\n",
    "\n",
    "        return torch.cat([dx1, dx2], dim=-1)\n",
    "    \n",
    "    def g(self, t, y):\n",
    "        return self.g_params\n",
    "\n",
    "# construct UDE model in Neuromancer\n",
    "net = blocks.MLP(2, 1, bias=True,\n",
    "                    linear_map=torch.nn.Linear,\n",
    "                    nonlin=torch.nn.GELU,\n",
    "                    hsizes=4*[20])\n",
    "sde_learnable = LotkaVolterraSDELearnable(block=net, batch_size=10)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We transform the shape of the Lotka-Volterra stochastic dataset from earlier to have a \"rollout\" of 2 steps (to mimic how training is done for the Neural ODE case)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "nx = 2\n",
    "nsim = 2000\n",
    "nsteps = 2\n",
    "nbatch = nsim//nsteps\n",
    "length = (nsim//nsteps) * nsteps\n",
    "bs = 10\n",
    "x_train_lvs = data_stochastic.squeeze(1)[:length].reshape(nbatch, nsteps, nx)\n",
    "train_data_lvs = DictDataset({'X': x_train_lvs, 'xn': x_train_lvs[:, 0:1, :]}, name='train')\n",
    "train_loader_lvs = DataLoader(train_data_lvs, batch_size=bs,\n",
    "                            collate_fn=train_data_lvs.collate_fn, shuffle=True)\n",
    "\n",
    "x_dev_lvs = data_stochastic.squeeze(1)[:length].reshape(nbatch, nsteps, nx)\n",
    "dev_data_lvs = DictDataset({'X': x_dev_lvs, 'xn': x_dev_lvs[:, 0:1, :]}, name='dev')\n",
    "dev_loader_lvs = DataLoader(dev_data_lvs, batch_size=bs,\n",
    "                            collate_fn=dev_data_lvs.collate_fn, shuffle=True)\n",
    "\n",
    "x_test_lvs = data_stochastic.squeeze(1)[:length].reshape(1, nsim, nx)\n",
    "test_data_lvs = DictDataset({'X': x_test_lvs, 'xn': x_test_lvs[:, 0:1, :]}, name='test')\n",
    "test_data_lvs_2 = {'X': x_test_lvs, 'xn': x_test_lvs[:, 0:1, :]}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We define our integrator which is a NeuroMANCER wrapper for torchSDE's sdeint. To support rollout of two time steps we give it a time step tensor of three values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "class BasicSDEIntegrator(integrators.Integrator): \n",
    "    \"\"\"\n",
    "    Integrator (from TorchSDE) for basic/explicit SDE case where drift (f) and diffusion (g) terms are defined \n",
    "    Returns a single tensor of size (t, batch_size, state_size).\n",
    "\n",
    "    Please see https://github.com/google-research/torchsde/blob/master/torchsde/_core/sdeint.py\n",
    "    Currently only supports Euler integration. Choice of integration method is dependent \n",
    "    on integral type (Ito/Stratanovich) and drift/diffusion terms\n",
    "    \"\"\"\n",
    "    def __init__(self, block ): \n",
    "        \"\"\"\n",
    "        :param block: (nn.Module) The BasicSDE block\n",
    "        \"\"\"\n",
    "        super().__init__(block) \n",
    "\n",
    "\n",
    "    def integrate(self, x): \n",
    "        \"\"\"\n",
    "        x (xn) is the initial datastate of size (batch_size, 1, state_size)\n",
    "        \"\"\"\n",
    "    \n",
    "        t = torch.tensor([0.,0.01, 0.02], dtype=torch.float32) # t is the time-step vector over which to integrate. Hard coded right now to support rollout=2\n",
    "        x = x.squeeze(1) #remove rollout dimension\n",
    "        ys = torchsde.sdeint(self.block, x, t, method='euler')\n",
    "        ys = ys.permute(1, 0, 2)\n",
    "        return ys "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We **attempt** to train the model using reference tracking and finite-difference loss workflow. Note that this model **will not** train, and this is meant to serve a motivating reason to utilize the **Latent SDE** framework."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 0  train_loss: 280370720.0\n",
      "epoch: 1  train_loss: 1259269.5\n",
      "epoch: 2  train_loss: 3173.81787109375\n",
      "epoch: 3  train_loss: 4029.0517578125\n",
      "epoch: 4  train_loss: 2160.29150390625\n",
      "epoch: 5  train_loss: 3204.1767578125\n",
      "epoch: 6  train_loss: 2170.798095703125\n",
      "epoch: 7  train_loss: 6954.16552734375\n",
      "epoch: 8  train_loss: 4428.49951171875\n",
      "epoch: 9  train_loss: 8502.1796875\n",
      "epoch: 10  train_loss: 6236.666015625\n",
      "epoch: 11  train_loss: 3221.912109375\n",
      "epoch: 12  train_loss: 9576.5546875\n",
      "epoch: 13  train_loss: 7514.75146484375\n",
      "epoch: 14  train_loss: 2029.9180908203125\n",
      "epoch: 15  train_loss: 3116.38720703125\n",
      "epoch: 16  train_loss: 3704.189697265625\n",
      "epoch: 17  train_loss: 6373.3974609375\n",
      "epoch: 18  train_loss: 3277.477294921875\n",
      "epoch: 19  train_loss: 2923.333984375\n",
      "epoch: 20  train_loss: 2974.04150390625\n",
      "epoch: 21  train_loss: 2931.277587890625\n",
      "epoch: 22  train_loss: 3578.0654296875\n",
      "epoch: 23  train_loss: 5337.3037109375\n",
      "epoch: 24  train_loss: 2434.29296875\n",
      "epoch: 25  train_loss: 6157.87646484375\n",
      "epoch: 26  train_loss: 3036.404296875\n",
      "epoch: 27  train_loss: 4701.6611328125\n",
      "epoch: 28  train_loss: 8118.04638671875\n",
      "epoch: 29  train_loss: 2118.330810546875\n",
      "epoch: 30  train_loss: 5187.4013671875\n",
      "epoch: 31  train_loss: 2097.62451171875\n",
      "epoch: 32  train_loss: 2507.549560546875\n",
      "Interrupted training loop.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "integrator = BasicSDEIntegrator(sde_learnable) \n",
    "model_sde = Node(integrator, input_keys=['xn'], output_keys=['xn'])\n",
    "x = variable(\"X\")\n",
    "xhat = variable('xn')[:, :-1, :]\n",
    "\n",
    "# trajectory tracking loss\n",
    "reference_loss = (xhat == x)^2\n",
    "reference_loss.name = \"ref_loss\"\n",
    "\n",
    "# finite difference variables\n",
    "xFD = (x[:, 1:, :] - x[:, :-1, :])\n",
    "xhatFD = (xhat[:, 1:, :] - xhat[:, :-1, :])\n",
    "\n",
    "\n",
    "# finite difference loss\n",
    "fd_loss = 2.0*((xFD == xhatFD)^2)\n",
    "fd_loss.name = 'FD_loss'\n",
    "\n",
    "# %%\n",
    "objectives = [reference_loss, fd_loss]\n",
    "constraints = []\n",
    "# create constrained optimization loss\n",
    "loss = PenaltyLoss(objectives, constraints)\n",
    "# construct constrained optimization problem\n",
    "problem = Problem([model_sde], loss)\n",
    "# plot computational graph\n",
    "problem.show()\n",
    "\n",
    "# %%\n",
    "optimizer = torch.optim.Adam(problem.parameters(), lr=0.001)\n",
    "trainer = Trainer(\n",
    "    problem,\n",
    "    train_loader_lvs,\n",
    "    dev_loader_lvs,\n",
    "    test_data_lvs_2,\n",
    "    optimizer,\n",
    "    patience=50,\n",
    "    warmup=0,\n",
    "    epochs=50,\n",
    "    eval_metric=\"dev_loss\",\n",
    "    train_metric=\"train_loss\",\n",
    "    dev_metric=\"dev_loss\",\n",
    "    test_metric=\"dev_loss\",\n",
    "    device='cpu', \n",
    "    epoch_verbose=1\n",
    ")\n",
    "# %%\n",
    "best_model = trainer.train()\n",
    "problem.load_state_dict(best_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "with torch.no_grad(): \n",
    "\n",
    "    # Initial condition\n",
    "    x0 = torch.ones([10,2])*10. #[batch_size x2] set of initial conditions\n",
    "    # Integrate the SDE model\n",
    "    data_stochastic_hat = torchsde.sdeint(sde_learnable, x0, t_span, method='euler')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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kJASg4g8MtZ2Cvqbf8yNO1O7HqunnWdffYxExKSSJSLWOnBg8duxYxo8fX+02R6bsnTp1KhMmTGDkyJHcd999xMTEYLfbefrpp6s9Yf/Yvwz/0fFmpzJ+Pz/B5XJhs9mYPXt2tdse7/yT0xUXF8fo0aO54oor6NChA59++invv/9+jQJBXZyrccTJ2gdq/nnExMSwatUq5syZw+zZs5k9ezbvvfce48aNY8qUKXVWs6e59NJL+fjjj3nmmWf44IMPKvVMlZeXM2TIEDIzM3nggQdo27YtgYGB7Nu3jwkTJlR8Lz755BOuvfbaSsc98hmMGTOG+++/n2nTptG3b1+mTZtGeHg4F110kVvfl8vlYsiQIdx///3Vrm/dunWl5yf6Hla3btSoUfz666/cd999dO3alaCgIFwuFxdccEGViQRqqy5f98i6//73vxUB9lh1HeZr+j0/Ubsfq6afZ2P9Hou4m0KSiFQrOjqa4OBgysvLGTx48Am3nT59Oi1atGDGjBmVflH429/+Vud1paSkYBgGycnJVX7pO9aRiRe2bt1a6S/JTqeTnTt30qVLl1q9vsPhoHPnzmzdurVimN/xfjlKSkrC5XKxdevWSpMdHDx4kOzs7Ioaj0xosG7duip/ja6NU/k8fHx8GDFiBCNGjMDlcnHrrbfy5ptv8uijj9KyZctTCngpKSksWbIEp9NZ0aNXV4601ebNmyv1BJaWlrJz585KP6Mnq3nkyJEMHTqUCRMmEBwczOuvv16xbu3atWzZsoUpU6Ywbty4iuV/HMo0bNiw4w5vio+P57zzzuOzzz7j0UcfZe7cuUyYMKFiCOSx7+WPNm3aRFRU1Al7kY73/lJSUsjPzz/p97U2srKy+OGHH5g8eTKPPfZYxfIjvTXucjqve+R7FRMTc8I2OfLztG7duhMe73S/56fqVD7Pk32PReTU6ZwkEamW3W7niiuu4PPPP6/2l4f09PRK20Ll3owlS5awaNGiOq/r8ssvx263M3ny5Eqvd+T1j0wL3LNnT6Kjo3njjTcoLS2t2Ob999+v0flEW7duJTU1tcry7OxsFi1aRHh4eMUwmCO/0P7xuEd6Dv44q9gLL7wAwMUXXwzA0KFDCQ4O5umnn6a4uLjKezpVNf08/jiFspeXV0Xv4JGpi4/33qpzxRVXkJGRwauvvlplXW3ex7EGDx6Mj48PL7/8cqVjvfPOO+Tk5FS05ZGaTzb8a9y4cbz88su88cYbPPDAAxXLq2s7wzCqTKccFxfH4MGDK92Odc0113Do0CFuuukmnE5nxVC7I/t27dqVKVOmVGrXdevW8d133520xykwMLDaz2PUqFEsWrSIOXPmVFmXnZ1NWVnZCY97ItW1C1T92a5rp/O6w4YNIyQkhKeeeqrSkNMjjvwbFh0dTf/+/Xn33XerfOePfd3T/Z6fqpp+njX5HovIqVNPkkgj9+677/Ltt99WWX7XXXfxzDPPMG/ePM4++2xuuOEG2rdvT2ZmJitWrOD7778nMzMTgOHDhzNjxgwuu+wyLr74Ynbu3Mkbb7xB+/btyc/Pr9N6U1JSeOKJJ3jooYfYtWsXI0eOJDg4mJ07d/LFF19w4403cu+99+JwOHjiiSe46aabGDRoEFdddRU7d+7kvffeq9E5SatXr+bqq6/mwgsv5NxzzyUiIoJ9+/YxZcoU9u/fz4svvljxC1yPHj0AeOSRRxg9ejQOh4MRI0bQpUsXxo8fz1tvvUV2djYDBgxg6dKlTJkyhZEjR3LeeecB5rkR//rXv7j++uvp1atXxXViVq9eTWFh4SkPmanp53H99deTmZnJoEGDaNq0Kbt37+aVV16ha9euFX8R79q1K3a7nWeffZacnBx8fX0ZNGgQMTExVV533LhxfPDBB9xzzz0sXbqUc889l4KCAr7//ntuvfVWLr300hPWnZ6ezhNPPFFleXJyMtdccw0PPfQQkydP5oILLuCSSy5h8+bNvPbaa/Tq1avSZBY9evTgk08+4Z577qFXr14EBQUxYsSIKse9/fbbyc3N5ZFHHiE0NJSHH36Ytm3bkpKSwr333su+ffsICQnh888/r3Ju0slcccUV3Hrrrfzvf/8jMTGR/v37V1r//PPPc+GFF9KnTx+uu+66iinAQ0NDmTRp0gmP3aNHD15//XWeeOIJWrZsSUxMDIMGDeK+++6rmCBiwoQJ9OjRg4KCAtauXcv06dPZtWtXxdTVpyokJIT+/fvz3HPP4XQ6SUhI4LvvvmPnzp21Ot6ZeN2QkBBef/11/vznP9O9e3dGjx5NdHQ0qampfPPNN/Tr168i0L/88succ845dO/enRtvvJHk5GR27drFN998w6pVq4DT/56fqpp+njX5HotILZzZyfRExFMcmS73eLc9e/YYhmEYBw8eNG677TYjMTHRcDgcRmxsrHH++ecbb731VsWxXC6X8dRTTxlJSUmGr6+v0a1bN+Prr782xo8fX2kq5iNTDz///PNV6jkyBXB6enq1df5x2t3PP//cOOecc4zAwEAjMDDQaNu2rXHbbbcZmzdvrrTda6+9ZiQnJxu+vr5Gz549jQULFlSZuro6Bw8eNJ555hljwIABRlxcnOHt7W2Eh4cbgwYNMqZPn15l+8cff9xISEgwvLy8KtXrdDqNyZMnG8nJyYbD4TASExONhx56yCguLq5yjJkzZxp9+/Y1/P39jZCQEOOss84yPvroo4r1x5sm+4/tXNPPY/r06cbQoUONmJgYw8fHx2jWrJlx0003GWlpaZWO/5///Mdo0aKFYbfbK03dXF07FhYWGo888kjF+42NjTX+9Kc/Gdu3bz9hex+Z3ry62/nnn1+x3auvvmq0bdvWcDgcRpMmTYxbbrnFyMrKqnSs/Px84+qrrzbCwsIMoOI9HzsF+LHuv/9+AzBeffVVwzAMY8OGDcbgwYONoKAgIyoqyrjhhhuM1atXG4Dx3nvvnfB9HOvKK680AOP++++vdv33339v9OvXr+LzHjFihLFhw4ZK21T383/gwAHj4osvNoKDg6tMZ5+Xl2c89NBDRsuWLQ0fHx8jKirK6Nu3r/GPf/zDKC0tNQyjdt9DwzCMvXv3GpdddpkRFhZmhIaGGldeeaWxf/9+AzD+9re/1bhdTjQF+Om87vH+rZg3b54xbNgwIzQ01PDz8zNSUlKMCRMmGMuXL6+03bp16ypex8/Pz2jTpo3x6KOPVtrmdL/nSUlJxsUXX1xtu/xxCnDDqNnnWdPvsYicGpthnOYYCBERERGLvfPOO1x//fXs2bOHpk2bWl2OiNRzOidJRERE6r20tDRsNlula0+JiNSWzkkSERGReuvgwYNMnz6dN954gz59+hAQEGB1SSLSAKgnSUREROqtjRs3ct9999GyZUvef/99q8sRkQZC5ySJiIiIiIgcQz1JIiIiIiIix1BIEhEREREROUaDn7jB5XKxf/9+goODsdlsVpcjIiIiIiIWMQyDvLw84uPj8fI6fn9Rgw9J+/fvJzEx0eoyRERERETEQ5zsmmoNPiQFBwcDZkOEhIRYWovT6eS7775j6NChOBwOS2tpiNS+7qX2dS+1r3upfd1L7et+amP3Uvu6lye1b25uLomJiRUZ4XgafEg6MsQuJCTEI0JSQEAAISEhlv+ANERqX/dS+7qX2te91L7upfZ1P7Wxe6l93csT2/dkp+Fo4gYREREREZFjKCSJiIiIiIgcQyFJRERERETkGApJIiIiIiIix1BIEhEREREROYZCkoiIiIiIyDEUkkRERERERI6hkCQiIiIiInIMhSQREREREZFjKCSJiIiIiIgcQyFJRERERETkGApJIiIiIiIix1BIEhEREREROYZCkoiIiIiIyDEsDUkLFixgxIgRxMfHY7PZ+PLLLyutNwyDxx57jLi4OPz9/Rk8eDBbt261plgREREREWkULA1JBQUFdOnShX//+9/Vrn/uued4+eWXeeONN1iyZAmBgYEMGzaM4uLiM1ypiIiIiIg0Ft5WvviFF17IhRdeWO06wzB48cUX+etf/8qll14KwAcffECTJk348ssvGT169Jks9fS5yknb8AvO7T+AUf17FhERERER61kakk5k586dHDhwgMGDB1csCw0N5eyzz2bRokXHDUklJSWUlJRUPM/NzQXA6XTidDrdW/QJHMzKo8mMP/Enm5NVa6+mQ+celtXSUB35fK38nBsyta97qX3dS+3rXmpf91Mbu5fa1708qX1rWoPHhqQDBw4A0KRJk0rLmzRpUrGuOk8//TSTJ0+usvy7774jICCgbos8RW3tzWnj2so333zJrj0HsdksLafBmjt3rtUlNGhqX/dS+7qX2te91L7upzZ2L7Wve3lC+xYWFtZoO48NSbX10EMPcc8991Q8z83NJTExkaFDhxISEmJhZVBYPh/WbCWldCOuxFsZ0TnO0noaGqfTydy5cxkyZAgOh8Pqchocta97qX3dS+3rXmpf91Mbu5fa1708qX2PjDI7GY8NSbGxsQAcPHiQuLijYeLgwYN07dr1uPv5+vri6+tbZbnD4bD8QwnscCGseYeB9tVc9t1WLuyUgL+P3dKaGiJP+KwbMrWve6l93Uvt615qX/dTG7uX2te9PKF9a/r6HnudpOTkZGJjY/nhhx8qluXm5rJkyRL69OljYWW1ZzTthQsvYm1ZGDn7+Pe8bVaXJCIiIiIif2BpSMrPz2fVqlWsWrUKMCdrWLVqFampqdhsNu6++26eeOIJZs6cydq1axk3bhzx8fGMHDnSyrJrzyeQXP9mAPT1Ws87P+8kPa/kJDuJiIiIiMiZZGlIWr58Od26daNbt24A3HPPPXTr1o3HHnsMgPvvv5877riDG2+8kV69epGfn8+3336Ln5+flWWfloMhnQG4PHgdRc5y9SaJiIiIiHgYS89JGjhwIIZhHHe9zWbj73//O3//+9/PYFXudTC0G20OzqS3azU2XHy4ZDfXnZNMYoS1M++JiIiIiIjJY89JaqiyA5pjOALwduZxZVIRznKDl37YanVZIiIiIiLyO4WkM8yw2THiugBwa6ssAGau2s/erJrN2S4iIiIiIu6lkGQBI6EnAM2LNtCnRSSl5S6e/XazxVWJiIiIiAgoJFnCiO9hPtj7G38d3g6Ar9fsZ+vBPAurEhERERERUEiyREVIOrSeDlHeDOvQBMOA//tsNSVl5dYWJyIiIiLSyCkkWSEkDoLjwHBB2hr+enF7wgMcrNmbwzs/77S6OhERERGRRk0hySrx3c37/StIjAjg0eHtAXjp+61sPqBhdyIiIiIiVlFIskqCeQFd9i4H4LJuCQxsE01JmYvbp62gqFTD7kRERERErKCQZJXE3ub97l/AMLDZbPzjyi7EBPuy9VA+z367ydr6REREREQaKYUkqySeBd5+kH8Q0s3pv6OCfPnHleY1lKYs2sWK1CwrKxQRERERaZQUkqzi7QvNfu9N2rmgYnH/1tFc0b0phgGPfrmOsnKXRQWKiIiIiDROCklWSu5v3m+bW2nxQxe1JcTPm/X7c5m6eLcFhYmIiIiINF4KSVZqO9y83zoXso6GoaggX+6/oC0A//xuC4dyi62oTkRERESkUVJIslJ0G2h+LmDAxq8qrRpzVjO6JIaRV1LGk7M2WlOfiIiIiEgjpJBktVZDzPvdv1RabPey8cSlHfGywf9W7efjpakWFCciIiIi0vgoJFktqZ95n7oIXJUnaejUNJQ7BrUC4IlvNnIgR8PuRERERETcTSHJanFdwBEARVmQsbnK6jvPb0XXxDDyS8r428x1GIZhQZEiIiIiIo2HQpLV7A5o2tN8/Ichd2AOu3v68k54e9mYs/4gn/229wwXKCIiIiJy6srKXTjr6eVsvK0uQIBmfc1rJe1eBL2ur7K6XVwIfxnSmufnbGbSzPX0ah5BclSgBYWKiIiIiFSVU+hk7saDzFy9H2eZiwO5xezJLMRmg26JYSRg4yKrizwFCkmeIKmPeZ+6CAwDbLYqm9w8IIUFW9JZsjOTuz5eyac39cHPYT/DhYqIiIhIY+NyGezIKGB7ej47MwrYfbiQ7MJSDuQWk1VQipfNxq7DBbiOc1bI0l1ZtAmt+vutJ1NI8gRNe4GXN+Tug+xUCE+qsondy8a/rurKhS8tZM3eHF78fisPXtjWgmJFREREpKEqdpbzw8ZDrNmXTXpeCZsP5LF+f26N9m0RHcjIrgk0iwggxN+b5pGB2Gw25m08wP5t691ced1SSPIEPoEQ2wn2r4T9K6oNSQDxYf4896fO3PTf3/jPwh2c2yqKfi2jznCxIiIiItJQHMotZtWebD77bS8rdmdxuKD0uNu2jwshwMeOv4+dAa2jaRruT6CvN5kFpTSLCKBbs/Bq92vauxmzMte56y24hUKSp4jrYoaktNXQ4bLjbja0fRMu6RLPzNX7uX/6Gmbe3o/IIN8zWKiIiIiIeDrDMDhcUMqmtDwOF5RwKLeE2FA/AEL9Hfy2O4ufNh9i9d6cKvuGBzjomxJFXKgfbWKD6ZgQSsuYIBz2xjPnm0KSp4jrYt6nrTnhZjabjScv68iavdnsOlzIzVN/44OJZ+Pvo/OTRERERBqj3GInO9MLyCwoZW9WIQu3ZrB0VybZhc4a7d8kxJdOCaFM6JtMi+hAYoJ98W5Egag6Ckme4khI2vcblJeB/fgfTbCfg/+M68nIf//Csl1ZPP7NBp66rNMZKlRERERE3GlvViGz1qax6UAeh/NLsXvZaB4ZSGFpGRn5JbgMyCt2UuYySM8rYW9WUbXHsdmgabg/TYL9iAnxJS2nGID84jJaNwlmQOtoBraJJibE70y+vXpBIclTxHYB/wgoyoQ9S6B5vxNu3qpJMG/+uSdj31nCtCWp9G8VzQUdY89QsSIiIiJyKnZmFFBYWkax00VWQSkxIb6E+jsID/ThcH4pa/Zms+VgHr9sO8yqPdmnfPzoYF8iA30I8vVmYJto+raMon1ciGZDriWFJE9h94aU82Dd57Dr55OGJIBzWkVxU/8WvLlgBw/OWEOnpqEkhPmfgWJFRERE5Fgul8Hmg3ms2ZtNQUk5Gfkl5BQ5yS8pY/OBPDYdyKvxsWw2ODs5grOaRxAb6k9BidmD5O9jJybYD7sX+Hh7Uex00SwigHZxIUQE+rjx3TU+CkmeJKmvGZJ2/1LjXf5vaBt+3X6YtftyuH7Kcqbf3IdAX32sIiIiIu5S7Cxn8Y7D/LItg4O5JWw5WLMQFPz772iJEQFkFpSSWVBKabmLAB87ieEB9GgeTru4EIZ1aEJMsIbAWUm/TXuSpN97j/YshbJS8D75XwR8vL14fWx3Rv77Fzam5fLENxt5+nKdnyQiIiJyOsrKXazdl0NaTjHOchclZS52ZhSwZm82i3dkUl7NlVO9vWw0iwwgMtCHlOggmoT4EehrJyzAh6HtmxAWUPl3O5fLoMhZrj9weyB9Ip4kqg0EREFhBuxbbvYs1UDT8ABevbo7o99azEdLU7moUyzntop2c7EiIiIiDUNJmYudeTB1SSo/bs5g2a5Mip2uE+4TGehD75RIOv8+PXaL6CASwvzx8a75rHBeXjYFJA+lT8WTeHlB8rmw/gvYuaDGIQmgd4tIxvdJYsqi3dzx0Uo+vP5sOsSHurFYERERkfqhqLSctftyOJRXzMHcEgB2ZRSwN6uQtJxitqfn4yz3hnWbKu3n77DTMSEEh90Lu5eN2BA/WsYEMbRDLM0jA7DZbFa8HTkDFJI8TXL/oyFp4IOntOsDF7Zl9d4cVu3J5q6PV/H1HedoRhMRERFpVHYfLuDnbRmE+DnILixl/pYMluw4TF5J2Un37ZwQQv/WMXRPCiMpMpDYED/19DRS+tQ9TfIA837PUigtBJ+AGu8a4OPN+9f2YvALC9h2KJ+XftjKAxe0dVOhIiIiItbIK3aSXWheJ6iotJxv16WxcFsGadnFHMgtrnaf8AAHKdFBhAX44O1lo3mUedHUlJggvG0u9q9dzMgRvXE4HGf43YgnUkjyNBEtIKQp5O6F1EXQ8vxT2j0swIenLuvIjf/9jTfnb+fcllH0bRnlpmJFRERE3Ce7sJTVe3PYmZ5PfJg/aTnFfL1mP8t2ZR13Hy8bdGsWjmEYBPh40yY2mAs7xtI1MQxve/XnCzmdTmZtcNe7kPpIIcnT2GzmkLvV08whd6cYkgCGdojl8u4JzFixj5um/sZXt59D86hANxQrIiIiUjf2ZBby/caDZBWUUm4Y/LQ5nQ1puRhVJ5EDzBl+DcOgzGXQNjaEy7rFkxwVRLdmYUQF+Z7Z4qXBUUjyRMeGpFp66rJO7MooYEVqNjdP/Y3Pbu5DsJ+6j0VERMQ6ecVOpi1JZduhfHKKnOw+XEhusRMvm4192UXV7pMUGUCrmCBSMwuJC/Wnd4tILuuWQGyoH4Zh4DLA7qUJFKRuKSR5ouT+5n3aKijKBv+wUz6En8PO62N7cPHLP7PpQB53frSSdyf00iwsIiIi4haFpWVsTMtjb1Yh/g47K/dks2THYQ4XlJISHUSxs5zfdmdRUlb91NpeNjgrOYKWMUEUlbronhTG0PaxRAcfv1fIZrNh16824gYKSZ4oNAEiW8LhbbD7V2h7Ua0O0yTEj/cm9OKKN35l3uZ0pi7ezZ/7NK/bWkVERKRRMgyDFanZzN98iPlb0lmzL+e4Q+N2Hy6seBwV5MvIrvEkRgTQJMSP6GBfbDZIigggUsPkxEMoJHmq5P5mSNo5v9YhCaBT01AeuKAtj3+9gUlfbaBjQijdmoXXYaEiIiLSWBzKK2bO+oMs3nGYHekFbEzLrbQ+OtiX5MhA8krKaBcXTO8WkYQH+LAxLZdAX296JIXTOSEULw2PEw+nkOSpkvvD8ndP67ykI67t25zluzKZve4A909fw9d3noOvt66fJCIiIjWzek82T8/eyJKdmZV6ixx2G0M7xDKgdTTntooiLtS/2v2HtG9yhioVqRsKSZ6q+e/nJR3aAPmHICim1ofy8rLx1GWdWLYri62H8nnp+63cr+sniYiIyB8Ulpbx2+4sft1+GD9vO+n5xazak826fUd7jLomhjGkfROaRwbSPSnsuMFIpD5TSPJUgZHQpBMcXAu7FkLHK07rcOGBPjwxsgM3T13B6/O30zEhlIs6xdVRsSIiIlIf5BY72ZNZyJ7MInKKStmZUUhaThHb0/PZkV5AYWl5tfvZvWxc0DGW+4e1ISlSlxWRhk8hyZMl9zdD0s4Fpx2SAC7oGMe4Pkl8sGg3d3+8igAfOwPb1L6HSkRERDxL6uFCdh0uwM9hp6CkDG+7jTKXwbq9OXy34SBr9+Wc9BhRQT6c3SISf4edIF9vmkcGcFGnOGJC/M7AOxDxDApJniy5Pyz+N+yYX2eH/NuIDqTnlTB73QFun7aSL2/rR8uYoDo7voiIiLifYRgUOctJyylm2c5M5qw/wKYDeaTlFJ9038hAH5qG+xPs56BFdCAJYf6EBTho1SSY+FB/moT46pIh0ugpJHmypL5gs0PWTshOhbBmp31Iu5eNl8d045r/LGHprkzGv7uUj27oTbPIgDooWEREROpCVkEpWw/ls2ZvNvuzi7HhYuN2L958bRF7s4rILynDdZzpthMj/LHbbAT4eHO4oARfbzuJEf70bxXNJV3jdQ6RSA0oJHkyvxBI6AF7l8L2edBjfJ0c1mH34t/XdGfUm4vYmVHAVW8t4qMbetM8SmOMRURErLAzo4BVe7L4ddthVu/NZsvB/Gq28gLyKi3xsXsRF+bHsA6xnJ0cQesmwSRG6A+fIqdLIcnTtTzfDEnbvq+zkATmdQw+ubE3Y/6zmO3pZlCadkNvUqI19E5ERMQd0nKK2JSWx2+7s1i/P4fsIieZBaUczi8lv6SsyvaBPnZ6t4ikeVQgBSVO9qSmckm/TvRoHkWInzeBvt4E+Ng1NE7EDRSSPF3LwfDT0+Z5SeVlYK+7jywmxI+Pb+zDNW8vZsvBfK75zxJm3t5PJ2aKiIicppKycnYfLuTnrRnM35LOrsMF7D5ceMJ92seF0L91NC2iA+nXMoqEsKPD4pxOJ7Nm7eKibgk4HA53ly/S6Ckkebr4buAfDkVZsG85NOtdp4ePDvbloxt6M+rNRWxPL2DsO0v46IbeRAb51unriIiINGSGYbAxLY+v1+znl20Z7EgvIO8PvUM2G7SKCaJNbAjdm5nXF4oI9CEi0IcmIb4E+yn8iHgKhSRP52WHFufB+hnmkLs6DkkAkUG+vDuhF6PeXMSWg/nc8MFypt3QGz+Hvc5fS0REpL7KLXbichlsPZTPzvQCDuQWU1BSRk6Rk6W7MtmRXlBpex9vL1pGB3Fuqyjax4fQu0UkTTRaQ6ReUEiqD1oONkPS1u9g0F/d8hJJkYF8eH1vLn/tF1akZnPPp6t4dUx3vLw0zllERBoPwzBYvjuL1XuyKSotZ39OEfkl5Ww+kHucyRSO8vH24rw20ZzTMor4MH/6t47GYfc6Q5WLSF1SSKoPWg0BbJC2GnL3Q0i8W16mZUwQb43rybh3ljJr7QEe9F3D05d3xq6gJCIiDVRhaRlr9+aQVVjKsl1Z/Lw1g80H8064T1yoH9HBvrSMCSIiwIdgPwfJ0YGc1yZaQ+ZEGgiFpPogKMacCnzfcnPIXfdxbnup3i0i+ceoLtz98Uo+Xb6XgpJy/nVVV3y89ZcwERGpn8rKXRzKK2He5kNsPpCHt5cXqZmFLNlxuMp5QwC+3l4MahtDiJ8Dmw2CfL1pExvM4HZN8HPY8ffRcHSRhk4hqb5IGWSGpB0/uTUkAVzSJR6Hl407P17JN2vTKC138ebYHhp6JyIiHiuzoLTi/KDt6fnsPlzI7sOFbDqQy/r9uSfcNzzAQUK4P+3jQujXMopzWkZpAiORRk4hqb5oMRAWPGdOBe5ygZd7e3Yu7BTH277e3PDBcuZuOMi4d5fyzoSe+Hrrr2ciImINl8sgo6AEwzCfL95xmPX7c1m6M5NVe7JPun+ziAAu7BSLYUC5y2BQ2xiig31pERWIt84dEpFjKCTVF017gSMQCjPg0HqI7eT2lxzQOprn/9SZBz9fy8/bMnhoxlr+eWUXXbRORETcrqi0nOW7M1m/P5c9mYWkZhayek82ucVVh8cd4WP3wtfbixYxQbRpEkRcqD8h/g7OaxNNdLCm2BaRmlNIqi+8faB5P3OGux0/nZGQBHBp1wQiAn2Y8N4yZqzYh6+3F0+O7KShdyIiUidW7clmzvoD7EjPJzWzCOP3bqJ9WUXVni90rBbRgfRLiaJjQgjntTF7hfSHPBGpCwpJ9UmLgWZI2vY99L3jjL3sua2ieeqyjjw0Yy0fLd1DqL8PD1zQRv8RiYhIjZW7DJbsPMzGtDyCfO3kFpUxd+NBlu7MPO4+sSF+tI8PoV1cMInhAXSID6VtXDA2wFlu4Ofw0v9FIuIWCkn1SesLYM7DsHMhFGWBf/gZe+mrejXDZrNx//Q1vDF/O3uzCnlpdDdNDy4iIpUUlpZR5jI4lFvCN2vS+H7jQWKCfdl0II992UVVtvf2sjGobQw9ksJp3SQYb7sNGzb8fbzomhh+3P9ndIqsiLiTQlJ9EpkCka3g8FbYvQjaXnRGX35Uz0RyCp08P2czX69JIzzAh79f2kF/xRMRaaRcLoNVe7OZszaNBRu9eH7jAvZmFx93+yBfbzo3DcUwYF92EZd0iWfM2c1ICPM/g1WLiJycQlJ90/yc30PSL2c8JAHc0L8FcWF+3D5tJf9dvJuC0jKeHNlJ14wQEWmAnOUuNqXlsSMjn9IyF/klZWxPz+fnrRkUlpZT5jLILCj9fWsv4GhA8rJBp6Zh9GkRiY/dRru4EM5rG4OfQ/9fiIjnU0iqb5qfA7+9Z04FbpHhneMpKi3ngc/XMGPFPg7llvD+tb00faqISD1U7CynqLScLQfz2Hoon/X7c8nILyGnyMnG/bknnTwh0MfOwDbRBBXsY/iAs2kdF0qovwNfb50vJCL1l0JSfdNiIGCDg2shdz+ExFtSxpU9E4kL9efG/y7n520ZPPzFWp65vLNmvRMR8UAZ+SWs3ZdDZn4puw8XsPVQPoG+3qzZm82Wg/kn3b9jQgiRgb4E+XkTHeRLj6RwkqMCKXMZtI0Nxo6LWbP20rtFBA6HptkWkfpPIam+CYyChB6wbzlsnQs9xltWyjmtovjHlV24bdoKPl2+l8LScp66vBMhug6FiIilyl0GG9NySc0s5NPle/hpc/pJ9/GyQbdm4XRvFoafw06ziABaxgTRMSEUx0lGCjidrroqXUTEIygk1Uethv4ekr6zNCQBXNQpjhev6srdn6zi6zVp7D5cyLsTehEd7GtpXSIijUVRaTmLdmSwdGcWO9LzKSwtZ/We7CrD5JKjAmka7k90kC/xYf64DIM2scGcnRxJRKAPDrtNw+NERH6nkFQftRoCPz1lXlS2rNS80KyFLu2aQEKYPzf+9zfW7sth5L9/4Z0JPWkbG2JpXSIiDcWezEIO5ZWwPT2fgznFZBaWsiO9gOW7MilzGZSUVe3J8fX2onlkIH1SIpnQtznNowItqFxEpH5SSKqP4rpCYAwUHII9SyD5XKsromfzCKbf3IfrpixnZ0YBV7z2K69c3Y1BbZtYXZqISL1R7jKYv+UQczccIrOghCKni+2H8qu9vtCx4kP9OKdVFB0TQvGxe9GpaShtY0N0LTsRkVpSSKqPvLwguT+smw67FnpESAJoER3EF7f25ZapK1i04zDXTVnObQNb8pchrfUftYjI78pdBjszCigoKWPboXxW781mb1YRuw8XkJpZiLPcqLKPzQYBDjtJkYF0TAghItCXiEAHrZoEE+jjTdfEMHy8NcOoiEhdUUiqr5LPNUPSzgVw3sNWV1MhLMCHKRPP4u9fr2fq4lRenbeNg7nFPHuFZr4TkcappKyceZsOMXvdAfZlFbEhLZfC0vLjbu/vsHN2iwh6t4gkzN9B86hAOsSHEKxJcUREzhiFpPoqub95v3c5lBaAj+eMNffx9uKJkZ3o3iycez9bzWe/7aXMZfDcnzqfdIYkEZH6rthZzqYDefyyLYP5W9LZmJZLXnHVaw0F+3rTLi6Elk2CSIoIoH18CM0jA4kP81fvu4iIxRSS6qvwZAhpCrl7IXUxtDzf6oqquLx7U+xeNu75dDVfrNxHXrGTV8Z0x99HV1sXkfrFMAwOF5SSkV/CroxCfB3mH3zmb07HWe4iItCHgpJy1uzNZvXe7CpD5mJD/OjdIoKzW0TSIymclOggBSEREQ+mkFRf2Wxmb9LqaeZ5SR4YksCc+S7I15tbP1zB9xsPMerNRfxnXE9iQ/2sLk1EpFq7Dxew6UAey3dlsjEtjx3p+ezPKT6lY0QE+tClaSiD2zehbWwwXRPDFYpEROoRjw5J5eXlTJo0ialTp3LgwAHi4+OZMGECf/3rX3UtBzDPS1o9zTwvyYOd364JU68/m5t+nyL80n//zNvjetGpaajVpYlII1dYWsbMVfvJLXZSVOri1+0ZLNmZedztvb1stI0LBqDE6aJdXAjNIwPIKnRS5nLRIT6U/q2iSYzw1/9TIiL1mEeHpGeffZbXX3+dKVOm0KFDB5YvX861115LaGgod955p9XlWa/577Pa7V8Fxbng57nXJerVPIIvb+3HdVOWsfVQPiNe/ZkxZyXy2PAOGn4nImfMwdxi5m7aS0FJGftzivlu/QEy8ksrbWOzQfu4EDrGh5pD42ICiQn2IzLIB3+HXeFHRKQR8OiQ9Ouvv3LppZdy8cUXA9C8eXM++ugjli5danFlHiIs0Tw3KWsnpC6C1sOsruiEmkUGMOPWvtz18Sp+3HSIj5buYWVqNq9d050W0UFWlyciDYxhGBSUlvPz1gx+2ZbOvLV29i6q2vPeNNyfXs0jMAyDHknhDGwTQ2JEgAUVi4iIp/DokNS3b1/eeusttmzZQuvWrVm9ejU///wzL7zwwnH3KSkpoaSkpOJ5bm4uAE6nE6fT6faaT+TI69dlHfakc/DK2kn5th9xJQ+qs+O6i58d3ri6Cz9uSufh/61n04E8/vzOEl6/uhvtfh/CUlvuaF85Su3rXmrf05NfUsbuw4VsSy9gy8E8dh8uZOmuLLIKj21PGzagTWwwieH+xAT70jEhhBGdYvF1VO7R1udwavTz635qY/dS+7qXJ7VvTWuwGYZR9ap1HsLlcvHwww/z3HPPYbfbKS8v58knn+Shhx467j6TJk1i8uTJVZZPmzaNgICG95fB+KzF9Nr1Gnl+8fzY7hmryzkl2SXw6gY76cU2bBicHWNwRXMXGn0nIn9U5oKScthbYKO4HPKckFVqY0eujfRiyHMefwhcmI9Bi2CD9uEG7cIMgnS5IRGRRquwsJCrr76anJwcQkKOf6qKR4ekjz/+mPvuu4/nn3+eDh06sGrVKu6++25eeOEFxo8fX+0+1fUkJSYmkpGRccKGOBOcTidz585lyJAhOBx19L90cQ7e/2qDzVWG89Zl5vC7euRwQSmP/m8DczceAiA+1I/nrujI2ckRp3wst7SvVFD7uldjbN+M/BIyC0oJ9XdwILeEjLwS0vNL8fG2UVZusGRnFnuziyh3GWw9lH/CC7AChAc4SIkOpHWTIJKjAkmJDqRdbDCRgT6UlZU1uvY9kxrjz++ZpjZ2L7Wve3lS++bm5hIVFXXSkOTRw+3uu+8+HnzwQUaPHg1Ap06d2L17N08//fRxQ5Kvry++vr5VljscDss/lCPqtBZHFDTrA7sW4tjxA/S+pW6Oe4bEhjl4a1xPvlqTxuSZ69mfU8zYd5czoW9z/jKkNaH+p95OnvRZN0RqX/dqiO1rGAabDuSxI72A7KJSdmUUsOVgPj9vy6DcdWp/p4sI9CEm2JfEiACCfL3pEB9C7xaRNIsMIMTv+O12ZLKFhti+nkTt635qY/dS+7qXJ7RvTV/fo0NSYWEhXl5elZbZ7XZcLpdFFXmo1heY10raPLvehSQwf3m5pEs8g9rG8OQ3G/loaSrv/7qL+VvSeW9CL5pHBVpdoojUULnLYH92Ed+sTWPF7izW7M3hQO7xrzEU7OdNXnEZAT52WkQHEh3kS2FpOd52G22ahNC1WRiGYZAQ5k+7uBACfT36vy0REWkgPPp/mxEjRvDkk0/SrFkzOnTowMqVK3nhhReYOHGi1aV5ljYXwnePwO5fPH4q8BMJ8vXm6cs7cWHHWO79bDU7Mwq4+OWFDOsQy+Xdm9KvZaSm3hWxSHZhKWv25rDrcAG/bMtgy8F8wgIcxAT7Uu6CImcZezKLSMspwlletXfI7mWjeWQAzSICSAj3p02TYLo1C6djQigul4GXLrQqIiIexKND0iuvvMKjjz7KrbfeyqFDh4iPj+emm27iscces7o0zxKZAhEpkLkddvwE7S+xuqLT0r91NDNu7cudH61kRWo2M1buY8bKfZzbKoqnLuukqXlF3KykrJz1+3NZmZrN6j3ZrN6bze7DhTXe38sGXRPD6N0iks5Nw4gN9aNpuD9RQVWHQgMKSCIi4nE8OiQFBwfz4osv8uKLL1pdiudrPQwWvwZb59T7kATQNDyAT2/qw/cbDzJvUzpfrNrHwq0ZnPePn7i8ewIjusTTp0Uk3navkx9MRCrZl13E/M3pLNl5mPziMiKDfFizN4f0PHPSm7ySMkrLqg5rTooMID7Un94tImndJAibDfZmFQEQ4u8gIcyf8AAfWjcJ0ndTRETqNY8OSXIKWg01Q9KWOeAqB6/6P4+2t92LCzrGcUHHOG4ZmMK9n61m+e4sPl2+l0+X78Vmg4s7xXHzgBTaxp7eNZZEGqJiZzk70gvYlp7P9kP5bEzL5ZdtGRScZJY4MCdI6JYYRrdmYXRuGkbnpqGEBficgapFRESsp5DUUDQ/B/zCoCAdUhdD835WV1SnmkcF8slNfZi74SDzNh1i9ro0covL+HpNGl+vSSMpMoAnL22P505oL+I+OUVOfth4kEXbD1Na7iKzoJSM/FI2Hcg97neiR1I47eNCCPbzxtfbTkSQDz2aheNtt+HvsNM03F/nAIqISKOlkNRQ2B3Q5iJYPQ02ftXgQhKYJ35f0DGWCzrG8sRlHVmzN4fnvt3E0l2Z7D5cyNh3l5MYaCe/yV4u7BRP5HHOfxCpzwzDIC2nmEXbD7PpQC6bDuSxbFcmxc7qZ/0M9XfQMiaIltFBpMQE0jE+lA4JobWaXl9ERKSxUEhqSNqNOBqSLngaGvBfgR12L3okhfPJTX3IKXLyzOyNfL5iH3sKXPz1fxv46/82cHEnc5hex4RQq8sVqbWsglJ2ZBSwK6OA3ZmF/G/VvmonUWgeGUDfllE0jwwgMtCXQF9vujULIybYVz1CIiIip0ghqSFJOQ8cgZC7F/avgIQeVld0RoT6O3j68s7cPSiFJ6b9wOr8IFIzzeu0fLM2jS5NQ3nwwnb0SYm0ulSRSgzDILvQSZGznPyiEpan21g8cwMlZQZhAT7sPlzAwm0ZVSZRsNmgVUwQfVOiaBsbTNu4EDonhGqWOBERkTqikNSQOPyh9VBY/4XZm9RIQtIREYE+DE4weOGic9mSXsgb83cwZ90BVu/NYcx/FtMxIYRrzk5iZNcE/H3q/8QWUn+4XAYHcs0hcv9bvZ+sglLsXjZSMwvJLCg9Zks7sLfK/tHBvjQJ8aVdbAjdmoVzSdd4gnRRVREREbfR/7INTbsRZkjaMBPO/1uDHnJ3Ih3iQ3llTDcO55fwr++38Omyvazbl8tDM9by0Iy1nNsqir4pUVzRI4GYYD+ry5UGwlnuMofFHS5kY1ou3204yIHcYnKKnNVOqX0sh91GqMPFRV2T8PPxxlnuIjkqkC5Nw+iSGHZm3oCIiIgACkkNT6uhYPc1Lyx7aCM0aW91RZaKDPLliZGduGdIGz5ZtofX5m0jr6SMhVszWLg1g5d/2MqFnWLpkRROx/hQfB1eNI8MxM+hniY5PsMwyCp0EuTrTUFJGb/tzmLuhoPMWptGXklZtft4e9lIjAigd4sIzmkZjbfdhp/DTvdmYQT5elNcUsp3c77loova4nBoUgURERErKSQ1NL7BkDIItsw2h9w18pB0RESgD7cMTOH6c5PZejCfxTvMYU+r92QzY8U+ZqzYV7FtdLAvE/o2Z1yfJIL99MuqmHak57NqTzY/bjrE4h2ZZOSXVLud3ctGq5ggmob7M6R9E5pHBhIX6k98mN8JL7Cqi6+KiIh4DoWkhqjtRWZI2jYXBj5gdTUexWH3on18CO3jQ7i2X3MWbT/MN2vTSM00h0dlFTpJzyvh+Tmbee+XnZyVHIHD7kVSZCDDO8fRuokuWttQZBWUsvFALgdzi1m4NYN1+3IwDCgtd2EYEOBjZ392EeGBPvh529l8MO+4x0qM8GdA62gu6hTH2cmR2DWBgoiISL2mkNQQpZxv3u/7DYqywD/c2no8lM1mo2/LKPq2jKpYVuws56OlqXywaDc7MwqYtfZAxbqXf9hK+7gQOsSH0DEhlGYRAfRJidTQPA+RllNEaZmLQ3klpEQHsXZfDvuyigDzXKEtB/PYdiifTQfyKCkrP+51hf4ot9gcPmezQbvYEM5rG83ANjF0jA8lr9hJYWk5SZEBmmZbRESkAVFIaohCEyC6LaRvgh3zocNIqyuqN/wcdq7tl8w1Zyfxzdr9HMotYV92EStTs1m7L4cNablsSMvls9/MGchC/LwZ2CaG89vFkBQZSGSgD4YBCeH+5JeU6YKddaikrJxVqdkYQLdmYazYnU1qZgEb0/KYvyWdnRkFp3zMZhEBNAnxpUvTMJpHBRLk643LMIgL9aekrJzYUD+yC51kFZTSo3l4lUk+NEuiiIhIw6SQ1FClDDJD0vYfFJJqwcfbi8u6Na20bF92EQu3pLMnq5C1+3LZciCPA7nFzFy9n5mr91d7nAAfO0G+3vRNiaRfyygiAn3YfbiQjgmh9EwKx8vLRlm5i8zCUnKLnAT4eOPvsBMe6HMm3qZHOpRbzIrULFakZvPlSvNcMYfdi4O5xZS5jBofJ8DHztnJEdi9vLB7QYvoIFo3CaJldDDBft5EBvnonDMRERGplkJSQ5VyPix+DbbPA8NotFOB16WEMH9Gn9Ws4nm5y+DX7Rl8vTqN7en5pGYWciiv8sn8haXlFJaW8+Wq/Xy5qmqQCvL1pshZTvkxv/zbbNA6JpjkqEBC/L2JDPKlVUwQ57SMIibE86crzy8pY9uhfPZmFRLi52BfdhF2m43isnJ2ZhTQPi6ErMJSdqQXUFrmorC0nNTMQtLzSzAMg4z80uMeO8TPm8LScspcBqH+DlrGBNEmNpgBraPpkxJJel4JSRFHh77p3CARERGpDYWkhiqprzkVeM4eyNgK0a2trqjBsXvZOLdVNOe2iq5YVlbuoqTMRU6Rk0Bfb7IKStmfU8T8zemsTM3mcEEJsaF+rEzNprC0nPxqpos2DNh8MK/KRAFeNmjdJJiUmCAiA30I9XcQ6u/AZRjYsNEiOpCYYD/8HF6kRAfhdRoBodxlYP+9l8vuZSMjv5TdGXlszrFhX3+QQH8fkiICCPL1ZkdGAb/tzuJATjF7sgr5dfvhk14T6ESOvM9OCaF0bRZGl6ZhlJS5iA/zIzbEj9ziMvZkFtIyJqjK+WAh6hkSERGROqCQ1FD5BEBSH9jxE2z/USHpDPG2e+Ft9yLQ1/xqhfo7aB4VSN+UqErbFTvLOVxQSmmZiwAfO5GBPnjZbJQbBul5Jazdl8P+7CKyC50cLihhyY5Mtv4+6cCmA8efZe2IED9vujULp0mIL62bBNMiOhAAZ7mBj7cXLaICiQ31Y/OBPH7cdIjUzEKyC52UlJWTX1zGhrRc4kL92ZddhGEYHO3ossOG1Sd9fS8btIoJptwwaBLiS7nLwMfbTm6RE28vGzYbNIsIJDbUl0Bfb2KC/WgbG4zNBkmR5rlBxxPq7yA0IfSkNYiIiIjUlkJSQ5YyyAxJG7+C3jdbXY0cw89hJyHMv8pyL2zEh/kT/4d1hmGw+3Ah6/fnsj+7iNxiJxn5JWw+kEdEoC/lLhd7s8zluUVl5BaXMX9L+mnVmJpZWOl5gI+dYHsZ8VFh5BSXkZ5XQn5JGZGBvvRuEUFUkC+h/g7OaRVFz6RwzfYmIiIi9ZZCUkPW4XL44e+w+2fYvxLiu1ldkdSSzWajeVQgzaMCT7ptWbmLjWl5rN6bze7DBWw5mM/B3GJ8vL1w2L0oKCljR4Z5PlCgj50+KZF0Twon1N9BucsgwMcbG+Dw9qJDfAhFv09x7WeHWbNmcdFFZ+NwmMPaDMNQGBIREZEGRyGpIQtLhHaXwPoZsGGmQlIj4W33olPTUDo1Pf6QtHKXOawvKsgHb7tXjY7rdDqrLFNAEhERkYaoZr8dSf3V8vcLy2773to6xKPYvWzEhvrVOCCJiIiINCb6Damha30h2H3gwBo4uMHqakREREREPJ5CUkMXGGleMwnMCRxEREREROSEFJIag/aXmPcbZ1pbh4iIiIhIPaCQ1Bi0vgBsdji4DrJ2WV2NiIiIiIhHU0hqDAIioFkf8/GmWdbWIiIiIiLi4RSSGos2F5r3W761tg4REREREQ+nkNRYHAlJu3+B4lxraxERERER8WAKSY1FZApEtgJXGWz/wepqREREREQ8lkJSY9J6mHm/ZY61dYiIiIiIeDCFpMbkyJC7rd+By2VtLSIiIiIiHkohqTFpehZ4+0PhYcjYbHU1IiIiIiIeSSGpMfH2geb9zMcrp1pbi4iIiIiIh1JIamx6Xmfer5uhIXciIiIiItVQSGpsUgaBTxDk7Yf9K62uRkRERETE4ygkNTYOP2g1xHy86StraxERERER8UAKSY1R2+HmvaYCFxERERGpQiGpMUoZBNjg0AbI3W91NSIiIiIiHkUhqTEKiICEHubjbT9YW4uIiIiIiIdRSGqsWg4277d+Z20dIiIiIiIeRiGpsWpzoXm/ZQ4UZlpbi4iIiIiIB1FIaqziukBsJygvgbWfWV2NiIiIiIjHUEhqrGw26DbOfLziAzAMa+sREREREfEQCkmNWecrwe4LB9fpwrIiIiIiIr9TSGrM/MOh/aXm41XTrK1FRERERMRDKCQ1dp1Hmfcb/geucmtrERERERHxAApJjV2LgWaPUsEh2Dnf6mpERERERCynkNTY2R3Q4XLzsYbciYiIiIgoJAnQbax5v/ErKMq2tBQREREREaspJAnEd4OY9lBWDOumW12NiIiIiIilFJLEvGZSlzHm441fW1uLiIiIiIjFFJLE1PJ8837PUih3WluLiIiIiIiFFJLEFN3OnOXOWQD7VlhdjYiIiIiIZRSSxOTlBSm/9yYte9vaWkRERERELKSQJEf1vcO8XzcdcvZZW4uIiIiIiEUUkuSo+K6QdA4YLlj4D6urERERERGxhEKSVDbwAfN+1UdQkm9tLSIiIiIiFlBIksqanwvhyVBWBNt/sLoaEREREZEzTiFJKrPZoO3F5uNN31hbi4iIiIiIBRSSpKp2l5j362ZAbpq1tYiIiIiInGEKSVJV4lmQ0ANcTtj0tdXViIiIiIicUQpJUpXNBh2vMB8veRNcLmvrERERERE5gxSSpHrd/gx+oXB4K+yYZ3U1IiIiIiJnjEKSVM8v5Ghv0voZ1tYiIiIiInIGKSTJ8R0JSRu/grJSa2sRERERETlDFJLk+Jr1gaBYKM6BbXOtrkZERERE5IxQSJLj87JDpz+Zj1dNs7YWEREREZEzRCFJTqzr1eb9ljlQkmdtLSIiIiIiZ4BCkpxYkw4Q0cK8ZtKO+VZXIyIiIiLidgpJcnKthpr3m2dZW4eIiIiIyBmgkCQn136keb92OuQdsLQUERERERF3U0iSk2vWG5qeBeUlsPpjq6sREREREXErhSQ5OZsNOl1pPt7wJRiGpeWIiIiIiLiTx4ekffv2MXbsWCIjI/H396dTp04sX77c6rIanw6Xgbcf7F8JuxZaXY2IiIiIiNt4dEjKysqiX79+OBwOZs+ezYYNG/jnP/9JeHi41aU1PkHRR6cDXznV2lpERERERNzI2+oCTuTZZ58lMTGR9957r2JZcnKyhRU1cl3GwPJ3YdM3UFoIPgFWVyQiIiIiUuc8OiTNnDmTYcOGceWVVzJ//nwSEhK49dZbueGGG467T0lJCSUlJRXPc3NzAXA6nTidTrfXfCJHXt/qOmqtSVe8Q5thy0mlbOM3GEdmvfMQ9b59PZza173Uvu6l9nUvta/7qY3dS+3rXp7UvjWtwWYYnnsWvp+fHwD33HMPV155JcuWLeOuu+7ijTfeYPz48dXuM2nSJCZPnlxl+bRp0wgIUM/H6Wq3/1NaH/yatNAeLG1xl9XliIiIiIjUWGFhIVdffTU5OTmEhIQcdzuPDkk+Pj707NmTX3/9tWLZnXfeybJly1i0aFG1+1TXk5SYmEhGRsYJG+JMcDqdzJ07lyFDhuBwOCytpdYObcDxn/4Ydh/KbvsNguOsrqhCg2hfD6b2dS+1r3upfd1L7et+amP3Uvu6lye1b25uLlFRUScNSR493C4uLo727dtXWtauXTs+//zz4+7j6+uLr69vleUOh8PyD+UIT6rllCV0gbgu2NJW45h9L1z9iTlFuAep1+1bD6h93Uvt615qX/dS+7qf2ti91L7u5QntW9PX9+jZ7fr168fmzZsrLduyZQtJSUkWVSQAXPYmeDlg6xxNBy4iIiIiDY5Hh6S//OUvLF68mKeeeopt27Yxbdo03nrrLW677TarS2vcYtpBt2vMx6s+srYWEREREZE65tEhqVevXnzxxRd89NFHdOzYkccff5wXX3yRa665xurSpMsY837jTCgtsLYWEREREZE65NHnJAEMHz6c4cOHW12G/FHi2RDeHLJ2mddN6jzK6opEREREROqER/ckiQez2aDzVebj1R9bW4uIiIiISB1SSJLaOxKSdsyDjK3W1iIiIiIiUkcUkqT2IlOg1TAwXPDb+1ZXIyIiIiJSJxSS5PT0GG/er/scXOXW1iIiIiIiUgcUkuT0tBwMfqGQlwY7F1hdjYiIiIjIaatVSDp48CB//vOfiY+Px9vbG7vdXukmjYi3L3T8k/n4h8nqTRIRERGReq9WU4BPmDCB1NRUHn30UeLi4rDZbHVdl9QnAx6AtdNh/0pY9SF0H2d1RSIiIiIitVarkPTzzz+zcOFCunbtWsflSL0U3ATO/Qt8PwmWvAVdrga7x1+CS0RERESkWrUabpeYmIhhGHVdi9RnnUeDIxAOroXN31hdjYiIiIhIrdUqJL344os8+OCD7Nq1q47LkXorJA7OusF8vHKqtbWIiIiIiJyGWo2JuuqqqygsLCQlJYWAgAAcDkel9ZmZmXVSnNQz3f4Mv7wI276H3P0QEm91RSIiIiIip6xWIenFF1+s4zKkQYhqCc36QOoiWP0RnPt/VlckIiIiInLKahWSxo8fX9d1SEPRbawZklZOhXPuAc18KCIiIiL1TK2nICsvL+fLL79k48aNAHTo0IFLLrlE10lq7NqPhG/uhcwdkLYK4rtZXZGIiIiIyCmpVUjatm0bF110Efv27aNNmzYAPP300yQmJvLNN9+QkpJSp0VKPeIbBK2GwMaZsP4LhSQRERERqXdqNbvdnXfeSUpKCnv27GHFihWsWLGC1NRUkpOTufPOO+u6RqlvOl1p3q+cCs4ia2sRERERETlFtepJmj9/PosXLyYiIqJiWWRkJM888wz9+vWrs+KknmpzEYQ2g5xUWPMp9NA5bCIiIiJSf9SqJ8nX15e8vLwqy/Pz8/Hx8TntoqSes3sfvWbSsv+ALjwsIiIiIvVIrULS8OHDufHGG1myZAmGYWAYBosXL+bmm2/mkksuqesapT7qNha8/eDAWtiz1OpqRERERERqrFYh6eWXXyYlJYU+ffrg5+eHn58f/fr1o2XLlrz00kt1XaPURwER0OlP5uNl/7G2FhERERGRU1Crc5LCwsL43//+x9atW9m0aRMA7dq1o2XLlnVanNRzvW4wJ29Y/yUMewqCYqyuSERERETkpGp9nSSAVq1a0apVq7qqRRqa+K7QtBfsXQa/TYEB91ldkYiIiIjISdU4JN1zzz08/vjjBAYGcs8995xw2xdeeOG0C5MGotcNv4ek9+Ccv5iTOoiIiIiIeLAa/8a6cuVKnE5nxWORGukwEuY8DLn7YPMsaK+JPURERETEs9U4JM2bN6/axyIn5O1rXidp4T9h6VsKSSIiIiLi8Wo1u93EiROrvU5SQUEBEydOPO2ipIHpcS3YvGDXQji0yepqREREREROqFYhacqUKRQVFVVZXlRUxAcffHDaRUkDE5YIbS4yHy9729paRERERERO4pRCUm5uLjk5ORiGQV5eHrm5uRW3rKwsZs2aRUyMpnmWavS63rxf/TGUlVhbi4iIiIjICZzSVGNhYWHYbDZsNhutW7eust5mszF58uQ6K04akOQBEBwHeWmwebY5oYOIiIiIiAc6pZA0b948DMNg0KBBfP7550RERFSs8/HxISkpifj4+DovUhoALy/oeg0s/AcseUMhSUREREQ81imFpAEDBgCwc+dOEhMT8fKq1SlN0lj1us6c5S51EWTtgvDmVlckIiIiIlJFra7smZSUBEBhYSGpqamUlpZWWt+5c+fTr0wanpB4aDEAdvwEaz6FAfdbXZGIiIiISBW1Cknp6elce+21zJ49u9r15eXlp1WUNGBdxpghadU0OOcesNfqR1BERERExG1qNV7u7rvvJjs7myVLluDv78+3337LlClTaNWqFTNnzqzrGqUhaTscfEMgayf89p7V1YiIiIiIVFGrkPTjjz/ywgsv0LNnT7y8vEhKSmLs2LE899xzPP3003VdozQkvkEw6FHz8U/PQEnVixKLiIiIiFipViGpoKCg4npI4eHhpKenA9CpUydWrFhRd9VJw9RjPIQnQ2EGrPiv1dWIiIiIiFRSq5DUpk0bNm/eDECXLl1488032bdvH2+88QZxcXF1WqA0QN6+cPbN5uOF/4TyMmvrERERERE5Rq1C0l133UVaWhoAf/vb35g9ezbNmjXj5Zdf5qmnnqrTAqWB6nUdBESavUnL3ra6GhERERGRCrWaWmzs2LEVj3v06MHu3bvZtGkTzZo1Iyoqqs6KkwbM7oB+d8Hcx+CHydB1DPiFWl2ViIiIiEjtepL+KCAggO7duysgyanpeydEtQZnoXndJBERERERD1DjnqR77rmnxgd94YUXalWMNDI2G/ScCN8+CL+9D72uN5eJiIiIiFioxiFp5cqVNdrOpl9y5VR0vgq+nwQH18G+36BpT6srEhEREZFGrsYhad68ee6sQxqrgAhoPxLWfAyL/g1X6gKzIiIiImKtOjknSeS09LnNvF//BeSnW1uLiIiIiDR6tZrd7rzzzjvhsLoff/yx1gVJIxTXGWI7w4E1sP0H6DLa6opEREREpBGrVUjq2rVrpedOp5NVq1axbt06xo8fXxd1SWPTaogZkjbPVkgSEREREUvVKiT961//qnb5pEmTyM/PP62CpJFqdwks/CdsngUFhyEw0uqKRERERKSRqtNzksaOHcu7775bl4eUxiK+K8R1gfJS+Gg0lBZaXZGIiIiINFJ1GpIWLVqEn59fXR5SGpMhfwdssHcpLH3T6mpEREREpJGq1XC7yy+/vNJzwzBIS0tj+fLlPProo3VSmDRCLQbC8H/B13fDsnegzx1gr9WPqIiIiIhIrdWqJyk0NLTSLSIigoEDBzJr1iz+9re/1XWN0ph0GQP+EZCzB5a8bnU1IiIiItII1erP9O+9pwt+ips4/OCsG2H+M/Djk9B9HPiFWl2ViIiIiDQipzWWafny5WzcuBGA9u3b06NHjzopShq5AQ/Aqg/N3qQ1n8JZN1hdkYiIiIg0IrUKSXv37mXMmDH88ssvhIWFAZCdnU3fvn35+OOPadq0aV3WKI2Nlxf0uR2+fQCWvwu9rocTXLxYRERERKQu1eqcpOuvvx6n08nGjRvJzMwkMzOTjRs34nK5uP766+u6RmmMulwFjkA4tAG2/WB1NSIiIiLSiNQqJM2fP5/XX3+dNm3aVCxr06YNr7zyCgsWLKiz4qQR8w+HHhPMx4v/bWkpIiIiItK41CokJSYm4nQ6qywvLy8nPj7+tIsSAeCs6wEbbP8R5j9ndTUiIiIi0kjUKiQ9//zz3HHHHSxfvrxi2fLly7nrrrv4xz/+UWfFSSMX0QL63mE+nvck7FthbT0iIiIi0ijUauKGCRMmUFhYyNlnn423t3mIsrIyvL29mThxIhMnTqzYNjMzs24qlcZp6OOQsxfWz4Clb8Flb1hdkYiIiIg0cLUKSS+++GIdlyFyAr1vNUPS6o/M85Sa9ba6IhERERFpwGoVksaPH1/XdYgcX2Iv6DoWVk2Fr/8CN/wIDn+rqxIRERGRBqrWF5MtLy/nyy+/rLiYbIcOHbjkkkuw2+11VpxIhUF/hc3fmFOCr5yqC8yKiIiIiNvUauKGbdu20a5dO8aNG8eMGTOYMWMGY8eOpUOHDmzfvr2uaxSBkDgY+LD5+NeXobzM2npEREREpMGqVUi68847SUlJYc+ePaxYsYIVK1aQmppKcnIyd955Z13XKGLqNhYCoiA7FX5+wepqRERERKSBqvXFZJ977jkiIiIqlkVGRvLMM88wf/78OitOpBKfABj2lPl44T+hUDMnioiIiEjdq1VI8vX1JS8vr8ry/Px8fHx8TrsokePqPApiO0FZMaz+2OpqRERERKQBqlVIGj58ODfeeCNLlizBMAwMw2Dx4sXcfPPNXHLJJXVdo8hRNhv0/P06XMvehrJSa+sRERERkQanViHp5ZdfpmXLlvTt2xc/Pz/8/Pzo168fLVu25KWXXqrrGkUq63Ql+EdA5nb4RT9vIiIiIlK3TmkKcJfLxfPPP8/MmTMpLS1l5MiRjB8/HpvNRrt27WjZsqW76hQ5yjcYLnwOZlwPv75iTugQEmd1VSIiIiLSQJxST9KTTz7Jww8/TFBQEAkJCcyaNYsvv/ySESNGKCDJmdXxCojrCiU55iQOIiIiIiJ15JRC0gcffMBrr73GnDlz+PLLL/nqq6/48MMPcblc7qpPpHpeXjDk7+bjlVM1052IiIiI1JlTCkmpqalcdNFFFc8HDx6MzWZj//79dV6YyEkl94cmnaCsSNdNEhEREZE6c0ohqaysDD8/v0rLHA4HTqezTos6nmeeeQabzcbdd999Rl5PPJzNBuc9ZD5e8ibkH7S2HhERERFpEE5p4gbDMJgwYQK+vr4Vy4qLi7n55psJDAysWDZjxoy6q/B3y5Yt480336Rz5851fmypx9pcBE3Pgr1L8Vr2H6C71RWJiIiISD13Sj1J48ePJyYmhtDQ0Irb2LFjiY+Pr7SsruXn53PNNdfwn//8h/Dw8Do/vtRjNhv0uwsAr9/exVFW9SLHIiIiIiKn4pR6kt577z131XFCt912GxdffDGDBw/miSeeOOG2JSUllJSUVDzPzc0FwOl0nrFhgcdz5PWtrqPBSRmCd1RrbBlb6LXzVZyll1ldUYOkn1/3Uvu6l9rXvdS+7qc2di+1r3t5UvvWtAabYRiGm2s5LR9//DFPPvkky5Ytw8/Pj4EDB9K1a1defPHFarefNGkSkydPrrJ82rRpBAQEuLlasUpw0V76b5mEt6uU5c1vZV94b6tLEhEREREPU1hYyNVXX01OTg4hISHH3c6jQ9KePXvo2bMnc+fOrTgX6WQhqbqepMTERDIyMk7YEGeC0+lk7ty5DBkyBIfDYWktDZEx/1l8fn4eV3A85Tf9Yl50VuqMfn7dS+3rXmpf91L7up/a2L3Uvu7lSe2bm5tLVFTUSUPSKQ23O9N+++03Dh06RPfuR0/GLy8vZ8GCBbz66quUlJRgt9sr7ePr61tpYokjHA6H5R/KEZ5US0Pi7HsHBUvfJzBvP14/PAaXvmp1SQ2Sfn7dS+3rXmpf91L7up/a2L3Uvu7lCe1b09c/pYkbzrTzzz+ftWvXsmrVqopbz549ueaaa1i1alWVgCSNnCOAlUk3YGCDlf+FLXOsrkhERERE6iGP7kkKDg6mY8eOlZYFBgYSGRlZZbkIwOGgtrjOvhn7ktdh5h1w21LwD7O6LBERERGpRzy6J0mkNlwDHobIlubFZec/Z3U5IiIiIlLPeHRPUnV++uknq0sQT+fwhwufhalXwJI3oNVgSBlkdVUiIiIiUk+oJ0kappaDocvVYJTD9ImQucPqikRERESknlBIkoZr+AsQ3hyKsuC7R62uRkRERETqCYUkabgc/jDyDfPxtu+hJM/aekRERESkXlBIkoatWW+IbAVlxbD4daurEREREZF6QCFJGjabDQbcbz5e+hYUZlpbj4iIiIh4PIUkafjajzSnBC9Ih2/usboaEREREfFwCknS8Hn7wBVvg80O67+ALXOsrkhEREREPJhCkjQO8d2gz63m4zkPg6vc2npERERExGMpJEnjMeAB8A+Hw9vg11esrkZEREREPJRCkjQevsFw3iPm45+egZy91tYjIiIiIh5JIUkal17XQ0IPKCuCz28Aw7C6IhERERHxMApJ0rjYbDD8X2DzgtRfIXWx1RWJiIiIiIdRSJLGJ64LdBtrPv7xcWtrERERERGPo5AkjdOAB8DuA7t/gaX/sboaEREREfEgCknSOIU2hd6/Twk+615dO0lEREREKigkSeM1eBL0nGg+nvMwlDstLUdEREREPINCkjReNhsMngwBUea1k5a9Y3VFIiIiIuIBFJKkcfMLgUFHrp30NBQctrYeEREREbGcQpJIt3EQ0x6Ks+F/t1pdjYiIiIhYTCFJxO4Nl/7bvHbSlm9h87dWVyQiIiIiFlJIEgFI6A5n3Wg+nnkH5B+yth4RERERsYxCksgR/e+HkKZQcAi+/gsYhtUViYiIiIgFFJJEjgiMhDEfgZc3bPoavroTysusrkpEREREzjCFJJFjxXWGi543z09a8QHM+j9wuayuSkRERETOIIUkkT/qOREufc18/Nv7MOteDb0TERERaUQUkkSq03UMXPYmYIPl75hhSUREREQaBYUkkePpMhrOf8x8PPdvkL7Z2npERERE5IxQSBI5kT63Q7M+UJIDH14JxTlWVyQiIiIibqaQJHIi3j5w1YcQ1gyyd8PiN6yuSERERETcTCFJ5GQCI2HQo+bjZW9D1i5LyxERERER91JIEqmJdpdAZEvzQrNv9If0LVZXJCIiIiJuopAkUhMOP7hqKjgCzPOTZt4BpYVWVyUiIiIibqCQJFJTMe1g3Eyw+8CexbDwH1ZXJCIiIiJuoJAkcioSe/1+/SRg4T9hxQfW1iMiIiIidU4hSeRUdbgMWl9gPp55Byx5y9p6RERERKROKSSJnCqbzZwWvNf15vPvHoGcvdbWJCIiIiJ1RiFJpDbs3nDRPyCpH5SXwrJ3rK5IREREROqIQpJIbdls0PtW8/GytyH/kLX1iIiIiEidUEgSOR1tLoS4rlCSC99PsroaEREREakDCkkip8PLbg67A1j1IexZam09IiIiInLaFJJETldiL+g21nw85xEwDGvrEREREZHTopAkUhcGPQbe/rB3KWyda3U1IiIiInIaFJJE6kJwE+h1nfn4swmw8WtLyxERERGR2lNIEqkrA+6HpHPAWQAzboRt31tdkYiIiIjUgkKSSF3xC4U/fwFNOplBaeoVsGqa1VWJiIiIyClSSBKpS94+MPFbaDvcfD7zTti5wNqaREREROSUKCSJ1DXfILhyCiT2BpcTPhgJv02xuioRERERqSGFJBF3sHvDuP9B6wvAKIev7oTvJ2t6cBEREZF6QCFJxF0cfjB6GnT8k/n85xdg1n3W1iQiIiIiJ6WQJOJOXnb40zsw5HHz+bK34dBGa2sSERERkRNSSBI5E/rd+ftkDgZ8fDUUZlpdkYiIiIgch0KSyJlywdMQGAOZO+C5ZPhkLOTstboqEREREfkDhSSRMyWsGVz9MTgCzOcbv4L3h0N5mbV1iYiIiEglCkkiZ1JCDxj7OfS53XyetRM2fWVtTSIiIiJSibfVBYg0Okl9zZvhgsWvwWcTwC8UUgZZXZmIiIiIoJ4kEev0uxuadDQfT58IWbstLUdERERETApJIlYJbgI3/Ajx3aEoCz79MxTnWl2ViIiISKOnkCRiJW9fGPUBBERC2mp4JhHWzbC6KhEREZFGTSFJxGphiTB2xtFZ76ZfC+u/tLQkERERkcZMIUnEE8R3hTEfQ/Nzzeez7tUFZ0VEREQsopAk4ilaDDCnB49qAwXpMOdhqysSERERaZQUkkQ8ibcvXPoqYIPVH8HW762uSERERKTRUUgS8TSJZ0HvW8zHX98NZSWWliMiIiLS2CgkiXiiQX+FoFjI2QNrp1tdjYiIiEijopAk4ol8AqHnRPPxzDvg63ugOMfamkREREQaCYUkEU91zt3QaRQY5bD8HfjvZZrxTkREROQMUEgS8VTevnDFf8wZ7/zDYd9v8M5QyNxhdWUiIiIiDZpCkoinazkYxv0PfILg8FZ4vR8sft3qqkREREQaLIUkkfogrgvcMA8Se4OzEL59EBY8b3VVIiIiIg2SQpJIfRHdGq6dBYMeNZ//+AT88HdraxIRERFpgBSSROoTLzv0vxcGPmw+X/hPWP+FtTWJiIiINDAKSSL10cAHoNtY8/FnE2D5e5aWIyIiItKQeHRIevrpp+nVqxfBwcHExMQwcuRINm/ebHVZIp7hon9CfDfz8dd3w+qPLS1HREREpKHw6JA0f/58brvtNhYvXszcuXNxOp0MHTqUgoICq0sTsZ7Dz5zMIekc8/kXN8FLXcBZbG1dIiIiIvWct9UFnMi3335b6fn7779PTEwMv/32G/3797eoKhEPYrPBqCnmkLtdCyFrF0y9HIY8Dk17WF2diIiISL3k0SHpj3JycgCIiIg47jYlJSWUlJRUPM/NzQXA6XTidDrdW+BJHHl9q+toqBpt+/qEwtUz8PpxEvbF/4bdv8Dbgygf9BiuPnfW2cs02vY9Q9S+7qX2dS+1r/upjd1L7etentS+Na3BZhiG4eZa6oTL5eKSSy4hOzubn3/++bjbTZo0icmTJ1dZPm3aNAICAtxZoojlgor30Xv7PwkszQBgb1hvfmt+M9g8emStiIiIyBlRWFjI1VdfTU5ODiEhIcfdrt6EpFtuuYXZs2fz888/07Rp0+NuV11PUmJiIhkZGSdsiDPB6XQyd+5chgwZgsPhsLSWhkjt+zvDwP7VbXit/RSA8gEP4zrnntM+rNrXvdS+7qX2dS+1r/upjd1L7etentS+ubm5REVFnTQk1Yvhdrfffjtff/01CxYsOGFAAvD19cXX17fKcofDYfmHcoQn1dIQqX2Bka9BeQls+B/2+U9jj2kNHS6rk0Orfd1L7eteal/3Uvu6n9rYvdS+7uUJ7VvT1/foMTiGYXD77bfzxRdf8OOPP5KcnGx1SSL1g90BV06Bjn8CDPjsWljyJrhcVlcmIiIi4vE8OiTddtttTJ06lWnTphEcHMyBAwc4cOAARUVFVpcm4vlsNrjsTWg1FDBg9v0w9TIoLbS6MhERERGP5tEh6fXXXycnJ4eBAwcSFxdXcfvkk0+sLk2kfrB7w6gPoOd15vMdP8H3k6ysSERERMTjefQ5SfVkTgkRz+bwh+EvQHw3mHk7LH8HErpDl9FWVyYiIiLikTy6J0lE6lC3sebkDa4y+OImmP+c1RWJiIiIeCSFJJHG4sg5Su0vNZ/PexJ2/WJtTSIiIiIeSCFJpDHx9jVnves61nz+zf9BWam1NYmIiIh4GIUkkcbGZoOhj4N/BKRvhBfawda5VlclIiIi4jEUkkQao4AIuPRV8PKGwgz4+BrYs9TqqkREREQ8gkKSSGPV9mK4bxs06QTlJfDpeFj9iYbfiYiISKOnkCTSmPmHw/iZENoM8vbDFzfCu0OhOMfqykREREQso5Ak0tgFRMDEb80pwgH2r4T3Lob8dGvrEhEREbGIQpKIQGgCXPpvuO57s3fp4Fr49gGrqxIRERGxhEKSiByV2AuumQ7YYN3nMCkUpoyAgsNWVyYiIiJyxigkiUhlTXvCsCePPt+5AF5oi9dPT+FdXmRdXSIiIiJniEKSiFTV5za45VdoP9J8Xl6K/ZcXGLruLmybvra0NBERERF3U0gSkeo16QCjpsBjWXDVhxihzXC4ivH+fAIsecvq6kRERETcRiFJRE7MywvaDafspp/ZF3aWuWz2ffDZtVDutLY2ERERETdQSBKRmnEEsLz5bZT3vt18vn4GzHkYysusrUtERESkjikkiUjN2Wy4zp8E/e4yny99Cz4ZC2WllpYlIiIiUpcUkkTk1A35O1z+Nnj7wZbZ8P7FsPFrMAxwuayuTkREROS0eFtdgIjUU52vhIBwmDYa9i6FT64xl9vs5sVphz0FbYeDzWZtnSIiIiKnSD1JIlJ7LQfDmI/B7nt0mVEO2anmMLxnkmDnQuvqExEREakF9SSJyOlpNRj+ehA2fGmemxTVCha/Bms/g5IcmDIc+t0NQyZbXamIiIhIjagnSUROn80GHS6DLldBQne44m24bwc06WSu/+VF2PiVpSWKiIiI1JRCkoi4R2Ak3PIzJPY2n38yFr6fDHkHra1LRERE5CQUkkTEvcbPhIgU8/HPL8A/W8M7Q2HJW1BaaG1tIiIiItVQSBIR9/L2hQnfQN87IKiJuWzPEph9H/z7LPjfbbBjvrU1ioiIiBxDIUlE3C8kDoY+AXesgP73mdOEA+TsgZVT4YNLYOYduiitiIiIeATNbiciZ45vEAz6q3nL3AmrpsH6L+DwVljxgXkD8AmCG38yZ8oTEREROcPUkyQi1ohIhkGPwB3L4Zrp4BN8dF1pPrwzBLbPg/xDYBjW1SkiIiKNjnqSRMR6rYbA3WsgfRP89j6s+QSKsuC/I831wfEw7EnoeLmVVYqIiEgjoZ4kEfEMARGQ1Bcufwse3g/dxwE2c13efpg+EfavsrJCERERaSQUkkTE8/gEwiWvwEN74YFd0KwvYMBbA+DHJ8BVbnWFIiIi0oApJImI5/INAv9wuOx1iGxpLlvwPLzUFVIXW1qaiIiINFwKSSLi+cKbw+3LYeiT4BsKOanw7jCY/aB6lURERKTOKSSJSP1gs0Hf2+Ge9ZByvrlsyevwXAtYN0Mz4ImIiEidUUgSkfrFNxiufA8SzzafF2fD9GthchhMCoXX+8G276Ekz8oqRUREpB7TFOAiUv/4hcJ130FZCXz7IKz6CMqKzHUH18HUK8zHXg7ochVc/C/w9rGuXhEREalXFJJEpP7y9oXh/4JhT5nXV1rxAeTsAy8v8zpLLiesnArZqRDfHZr2gtYXgF3/9ImIiMjx6TcFEan/HP7Q+xbzBuBywYr3Ye3nsPtn2LnAvIE5W97gydDtz2aYEhEREfkD/YYgIg2Plxf0nAgTvoaBD0FY0tF1RVnw1Z0w7wnr6hMRERGPpp4kEWm4bDYY+KB5A0jfAt//DTbPgoX/hG0/mNdf2r8C+txuBiubzdqaRURExHLqSRKRxiO6NYyeZgYigLRVsG46ZO6Ab+6BV3vBnEdg72+aUlxERKQRU0+SiDQuNhsMfcI8N2n/SvN8pj1LzMkdDm+FRVth0asQ1wXGfAIhcVZXLCIiImeYQpKIND42G/S/t/Ky9C2wayGkLoKNX0HaanizP1z+JqQMsqZOERERsYSG24mIgDkUr9d1cMXbcMuv4BsCBYfgv5fBLy9DSf7RbYuyzRn0REREpEFST5KIyB9FpsA1n8EnfzaD0txHzZtfKLQaCms/M7eL7w4tBkDn0RDT1tqaRUREpM6oJ0lEpDrNesP/bYL+9x9dVpxzNCCBOSvez/+C186GaaNh3tPmFOMiIiJSrykkiYgcj5cdBj0C9++EC587urzzVTDxOxj+L2g5BLDBltkw/xl4vhXs+MmqikVERKQOaLidiMjJBETA2TdBjwlmb1JQjLm82dnmtZUOrIWVU2HJG+BywgeXQnJ/87ylgkNw/t+g3XBL34KIiIjUnEKSiEhNefseDUjHiu0EFz4Lg/4K714IB9fCzgVH139yDQQ1+b3XCUg5DzpeoQvXioiIeCiFpN+Vl5fjdDrd+hpOpxNvb2+Ki4spLy9362s1RmeifX18fPDy0ihVOQ7fYLjqA5j3FBzeDlk7j56jlH8QVk01H6+aCp9fByPfgE5/ArvDuppFRESkikYfkgzD4MCBA2RnZ5+R14qNjWXPnj3Y9BfkOncm2tfLy4vk5GR8fHzccnxpACJamNOIH2EY8Nt7sPlb2Dqn8rZf3gw/PQXDX8SWe4C+W1/FtqEUOl0OpfmQtcs8XnEuhCac0bchIiLSmDX6kHQkIMXExBAQEODW8OJyucjPzycoKEi9EW7g7vZ1uVzs37+ftLQ0mjVrpqArNWOzmect9ZxoPjcMWPMJzHsSslPN29TL8QaiAb643rz9kV8oNOkELQeZ13AKT4aW52vInoiIiBs06pBUXl5eEZAiIyPd/noul4vS0lL8/PwUktzgTLRvdHQ0+/fvp6ysDIdDQ6SkFmw26DLavJUWwJyH4bf3T75fcQ7s/tm8HdFljDnrns0Gdh/znCkRERE5bY06JB05BykgIMDiSqS+ODLMrry8XCFJTp9PIIx4Cfrfh9Pmy6wff+Hi1g68v70PmnSEgQ+YPU0BUfDj47BnSeX9V39k3gCC4+HytyD53MrblBbA9nmwc7557ae2w4+GqcJM8AmCnD2wdS407QUJ3c11hgEY5jToR7hckLoI/EIgpgPojz0iItJANeqQdISGTUlN6WdF3CK0KTidYLNhtBoK7S8+ui6hh3l/3XfmfXEu2Lxg63fwzT1HJ4bI2w9TRpgz7PW5DfIPwbK34bcpUJJjbrP0LfM+sTccXA+leYDN7IkyXL+/oA0wjr5+WBIkngUh8bDmU8hLO7ouuh30v9ecfEJERKQBUUgSEalP/ELM+46Xmz1DWbvM6cV/fsG8VtOPj5u3Y/kE/x6Ifrdn8TErjd97jY55fqzs3eatOukbzVn61n4GLQfDoY3gLDKDmiaaEBGRekwhSU7bDz/8wO233866deuw2+0n36EGvv32Wx588EFWrFih87dEjick3rwBXPpvSOgJs+4zL2gL5nlK/e+Dc+4BuzdkbIVN30BZsTldebPeZk+Uf7g5fC71V3PYXs5ecBZCeal5v3uRGYRaDIDzHzOPU5oPcx4x77d8a96OWD0Nzr4ZOo2Cpj3OfLuIiIicJoWkemjChAlMmTIFAIfDQbNmzRg3bhwPP/ww3t5n/iO9//77+etf/3pKAenOO+/kl19+Yd26dbRr145Vq1ZVWn/BBRfw6KOP8uGHH/LnP/+5jisWaaB6XmueU7R1LrS9GGLaVV4f1QrOufv4+6cMMu9j2lZe3n0cXPrq0fOT4rqY9z0mwPov4IfHoTgbwpvDvt/MdUvegCVvwvAXoPNoWPRvszcrqjW4yqH5ORCZUvl1ctPM0BXV6tTfu4iISB1SSKqnLrjgAt577z1KSkqYNWsWt912Gw6Hg4ceeqjKtqWlpW67rs/PP//M9u3bueKKK05534kTJ7JkyRLWrFlT7foJEybw8ssvKySJnIq4LkdDTF3yOs4fQTpcZt6OKMiABf+AVR9CSS58/RfzVp3WF5oX0g1NhMztlXujvP3Nc6vOe0QTRIiIyBmn/3mOYRgGhaVlbr0VlZZXu9wwjJMXeAxfX19iY2NJSkrilltuYfDgwcycORMww8XIkSN58skniY+Pp02bNgDs2bOHUaNGERYWRkREBJdeeim7du0CYMGCBTgcDg4cOFDpde6++27OPfcPs2Ud4+OPP2bIkCH4+flVtOHgwYMZNmxYxXvKzMykadOmPPbYYxX7vfzyy9x22220aNHiuMceMWIEy5cvZ/v27afUNiJiocAouPAZeDAVOlxezfoYcxggwJbZsHEmLP535YAEUFYEC/8Bfw+HA2vdX7eIiMgx1JN0jCJnOe0fm2PJa2/4+zACfGr/cfj7+3P48OGK5z/88AMhISHMnTsXMKc7HzZsGH369GHhwoV4e3vzxBNPcMEFF7BmzRr69+9PixYt+O9//8t9991Xsc+HH37Ic889d9zXXbhwIVdffXXFc5vNxpQpU+jUqRMvv/wyd911FzfffDMJCQmVQlJNNGvWjCZNmrBw4UJSUlJOvoOIeA6bDS57A5r3Ay+HeU0n79/DUbkTFjwPOfvMXqId882hen3vgLRV5nDBrN2Q//sfbd44BwIiocVAKCuBzJ3mOVUXPeeeXjMREWn0FJLqOcMw+OGHH5gzZw533HFHxfLAwEDefvvtimF2U6dOxeVy8fbbb1dMY/3ee+8RFhbGTz/9xNChQ7nuuut47733KkLSV199RXFxMaNGjTru6+/evZv4+PhKyxISEnjzzTcZN24cBw4cYNasWaxcubJW50vFx8eze/dxZtYSEc/m7Qu9rq+63O6A8x6ufp9WQ8zJJsAMTzNuNMNS4WFY93nlbd8ZBmffZA73O7wNfvi7OcQvpoMZ0koLYMAD0OaCE9fpLDKnVdfFeEVE5HcKScfwd9jZ8Pdhbju+y+UiLzeP4JDgKjO2+TtObVa4r7/+mqCgIJxOJy6Xi6uvvppJkyZVrO/UqVOl85BWr17Ntm3bCA4OrnSc4uLiiuFsEyZM4K9//SuLFy+md+/evP/++4waNYrAwMDj1lFUVFQx1O5YV155JV988QXPPPMMr7/+Oq1a1e5EbH9/fwoLC2u1r4jUcy0GwL2bzeF2C1+AgnTzmlKRKbDtB/PCtr+8aN6Otfvno48/ugoA7+A4zrbFYP/ofdjxozmjnyPQnB3wwFpzeN/Zt0CnKyEiGXxDzBkBRUSkUdL/AMew2WynNeTtZFwuF2U+dgJ8vE97WuvzzjuP119/HR8fH+Lj46v00vwx2OTn59OjRw8+/PDDKseKjo4GICYmhhEjRvDee++RnJzM7Nmz+emnn05YR1RUFFlZWVWWFxYW8ttvv2G329m6despvrujMjMzK+oTkUYqthNc+V7lZf3+Aj9Mhl9fPrrMJwhGvATpm6EwA9bNMGfdA2x5acSSBrm/b1uUZd5y9x7df8nr5u2IkKYw+G/Qaij4h7njnYmIiIdSSKqnAgMDadmyZY237969O5988gkxMTGEhIQcd7vrr7+eMWPG0LRpU1JSUujXr98Jj9utWzc2bNhQZfn//d//4eXlxezZs7nooou4+OKLGTRoUI3rhaO9XN26dTul/USkEbB7w9DHzdnvCtLN2fGadDQnjjjion/CgTWw4yfKs1LJ2/ADYUW7wNsPet8KuxZCXFdI6mvOyrdxprnsiNy9MOMGs1dpxIvQ5mJz2F9g9NHzq84kw4C8NAiOM4cT/nHdH5eJiEitKSQ1Etdccw3PP/88l156KX//+99p2rQpu3fvZsaMGdx///00bdoUgGHDhhESEsITTzzB3//+95Med9iwYRXXbDrim2++4d1332XRokV0796d++67j/Hjx7NmzRrCw8MB2LZtG/n5+Rw4cICioqKK6yS1b9++Ypjg4sWL8fX1pU+fPnXYEiLSoDj8ICzRvP2RlxfEd4X4rricTua7ZnHRRRfhcDiqP9bZN0JJPuxbDuu/NK/5lL7JPM9p+sSj2wXFwqBHoOvYupuevDgXMrZAdBtzUopjl6cuhpX/he0/mteRim4HIXHmemexef2pg+uh+blw+VsQHFs3NYmINGIKSY1EQEAACxYs4IEHHuDyyy8nLy+PhIQEzj///Eo9S15eXkyYMIGnnnqKcePGnfS411xzDffffz+bN2+mTZs2pKenc9111zFp0iS6d+8OwOTJk/nuu++4+eab+eSTTwCzx2r+/PkVxznSW7Rz506aN28OwEcffcQ111xDQEBAXTWDiMiJ+QaZs+i1GGg+z9wJX9wMexYf3Sb/AMy8A35+0Tw/qu3F5tC9g+sh/xAENYFz/6/qRXmrk50Km2bBT09BcY65zO4D5aXH3yd9o3n7o53z4Z9tIKEndL0a9q8wZwm85GWI+MPlFopzzeGJRrk5kYaIiFSikFQPvf/++7VaHxsbW6XXpzr79u3joosuIi4u7qTbRkREcPvtt/PCCy/w5ptvEh0dXeVaSw6Hg+XLl1dadrJznTIyMpg+fXqV/UREzqiIZLhuDhxYB2s/hZj2ZnBa8Lw5xC9zO2z9rup+az8FvzDznKgW54EjANpeBAFRkNDDDCZf3w3rv6i6b3UBqVkf6Hkd+IWa+5UVmz1KbS4we5MOrDGnTi8rMnvC9h3zb+e7F8LwFyBtNaz9DPwjzABluMz1CT0hrJl5zLguZsA7leBUlIVtx8/4lR4++bY1VZgJ854ye/E6/glaDjZ77dLWwMJ/muGu+bnQ9Roz2IqI1DGFJKmQk5PD2rVrmTZtWsWFaWvikUce4bXXXsPlcp32hBRH7Nq1i9dee43k5OQ6OZ6IyGmJ7Wjejmg3Ajb8z5yWPCjGPM+pJA8Sups9Q7l7KyaNYMc8837zN9UfO7IVdBgJ3cfD1jmQu9/sVfL2M4/X5mJzWOER91Q9DxSAslKYfT+snW4GrfhuZg9Y/gH4+OpjNtxReb9jQ9XmWbD4NTOABcWY532FNDXDin84uMrg4DrYvxJaDjGD12fj8XaVMQQvbIfeNHutWpxnBri01eY5XE3POv7QxIxt4GU3r5Vls5nt+FJXKPm9Z23NJ+YU7UdC3REbvzLfb1Rr6HeXeS0ur1ObKVZE5HgUkqTCpZdeytKlS7n55psZMmRIjfcLCwvj4YePc82TWurZsyc9e/as02OKiNSZI6Fp0CNV1w2eBJtnm9du8gkye3kOboCDaytv5xcGo6eZF9w9orrrStWUt485wcTwf5lhxu4we2R+fgHWfAr5B83tuo2FzqPN8LLtezPElZXAniVmuCrOOTq8cGPN/2Dmhcs8hyt9kxm2Zt93dGVgNCQPMIcExnczA4+r/P/bu/PoqIr0b+Df293p7AsQskFC2EFIYlgnKC4sAfRlUQdlOQwgg4pBRRCBGSU6w4wyiAdQhkUN4EEFmRE3GJAtyCZoCDsTIL8QYMgO2Zfu9K33jzJNN+nOZjoJyfdzTh/Tt+tW131SXO+TqltXrkKYbhGX7o/Je7AqEiQnd8BYVDlBskyasi8B38TI+8jGrJLLuhMR/UZMksisuilwRERUA3p3IOz3tj9TVbnQQvpZoFVHwLtd/X+/otyZLufWGoheAgx9SyZJXkHWq+D59QAGzb7z3mSUD/EtygR+iQNu/Fz99/l0gHHK9/hp1xYM8smG9voxOdokVDnN0FgsVyA89y9Z3tb0xApJO389Bg0w6h/AgJnyYb9X9gL/Oymn3VUklUIAP60Bdi+S76/sAVb/DhjxN5kIcrU/IvoNmCQRERE1FI1G3lcU+mDDfq9WV7OETOsEdB0mfw6fIEfBrh0D/HvJaXDGEjkS1LoTcOMXmQgFDwDKy3HLozvU6FehdXKSI1OZF4C2PeTzqg4tl/t7+supdyc3ydEuQCZDFdMXr+yVidWA54COg+XnTq7y856jrduqKEDUi8DvZgGJm+Uzs7IvAd/OltMgx6yS91oREdUBkyQiIiKqzGIJdZuC+9vfV+cskykAiJwsX5b+3/uV9/ndLPmqLUUB+kyR9yT99E/gwN/kFMIVYYB3MPD4cqDbCOt9jCXyocPGYjntL/RBjjwRkZV7IklavXo1li1bhvT0dEREROCDDz7AgAEDGrtZRERE1FRodcADL8v7mr6JkfdV5V0HPn9ajkKN+VBOcyxIB354Qy5oUaFtT6Dzo3KqZN/pct+iHMDVB+j4kP1nT5mMgKKVCWXmf+Wqhp2HVB4pVFW5mEfaGflML72HnG6Zd03em6Z3l6N45WVyxO3KPqD7KMDF/sPficixmnyStHXrVsydOxdr167FwIEDsWLFCowYMQJJSUnw8/Nr7OYRERFRU+LbBZj+HyD1MLB/iVyQ4uJ38mWP5bOnflxm/ZmiBfrPkEuOd40Gbl8FvpwC5CTL0aeKaYMVDi2XI1guPoBqlNMr08/KUauq6D3kw4Ir+HSQiVvv3wOmMihlJQjOOQztt98DhiK52mD4eMC3u1wk5Ow2wK8nEP6MvBetwv8SgPw0mXRptHIUrSBNTpksypbbXFvZb1dZAVCQIe8za99PLsKhc676WOwxGWUyKIRsh97GcxCNJXKKJQGGYtknPHi92xiafJL0/vvvY+bMmZg+fToAYO3atdixYwfi4uKwcOHCRm4dERERNTkajRwBmrYD2P0n4MT6O5/5dgNCfgcMjZXLrBdmAOe/kqM3145VrkuY5P4n1svkp/iWXHEPAISN71a0cgQr73rt2myZIAFAbiqQsFG+IC/Y+ty9z6nNlevZtfDOghmW2vYE7p8IHP1ALqTh3lYeizDJByCPXQ10HS4TmdJ8IC0ROPExcOk/ttur9wCmfif/C8jE5lYyoNHJ0TqNDsi8KJevP71VJkTpZ4GAcLnMfcFNQOsMmMrk70Q1AXk3ZGLZpqscjdO5yIVOuo6QI4T19JiRWjGVAxA1e3ZYucF64ZS75V6XSbvffUDqEfnwaUUjn/WlcQJup8jPjSUyyb6ZKOMTOhgICJPPSmvTWf7+Tn8hf08DZtof6awvhiL5u9BoZcLs7tsilttv0kmSwWBAQkICFi1aZN6m0WgwbNgwHDtm40QGoKysDGVlZeb3+fn5AACj0Qij0WhV1mg0QggBVVWhqnctL+oAQgjzfxvi+1qahoivqqoQQsBoNEKrbf4nCEsV/37u/ndE9YPxdSzG17GadHyH/x2ImAIl4yxEQLhcTMKSVwgQNUe+jCVASa78y32GvKBXTm2GbudcWfbXxEe4+8H08ELA3Q9KwU0oZ7dBtO8P9aHXgbICaM5tg+bAEihChfDtBrXPdKi9ngTUcihX9gBubSBad5Z16lzkSFHmRflAX3c/CM9AaC5+Dc3JjVCKssxNLdX5wKU8F0Kjg3L3CJYlW6NWWReBPYvvvLeoF4UZwGd2VmT8lXBtDaXk1p0NhkLgo0er3Mcmywcdm369Xsu+ZF0mO0m+Kpz89M4ug16B+uA8mQjWI3MfLsoDyvMBr/ZQUg9D+91LgLEIavgEAAqUsgIIDz85wubkDrh4Qbi2hub0Z9DcOAHh5gv1wdcgAnoDhZlQ8q7LpfXLCqA9tqpujbt6SL5++mflzw69BzUwEqLHaECrg2h7H0THh4HcVCgZZ+UDrNVyKNmXIdx9IQLC5FTP6hTnQLvnz1DSTsuRSs8ACN9u0KQchPDrhfLxn8pnqNUwWWpK54iatkERFVeWTdDNmzfRrl07HD16FFFRUebtr7/+Og4ePIjjx49X2uett97C22+/XWn7559/Djc3639QOp0OAQEBCA4Ohl6vr/8DaAZefPFF5OXl4bPPPmvsplg5ePAgXn/9dRw9erTekpW4uDj88MMP2LJli90yBoMB169fR3p6OsrLq/gfFBERNRsa1QA3QzZCcg6i0CUI//MZCJPWpfodfysh4GK8jXKtK8q11lPQvEquocSpNVwNOSh0CYSq0UOjGhCYm4C2hRcAIVCs90WGdwR0pjKE39gEjWpEmk9/ZHn2RPvbx5Dl2RtlOm90yIlHu9wTNptwrfWDON9uIgxadwTkJUJVdGhddBndM2r2DC0VGpQ5eSPXLRRFej+U6NtCKECWZxjcyzLgXXwV5VoXFOvbolzrAkWY4Fmajo5ZP8DdkIUcj27wLUyyqrNM64FSvTz2HI8euO3WEW0KL+GK/2O45d4FqqaW13RChW/hRYTkHEK728ehgal2+9eBSdEhx6M7/ArOm7eleffFbbdOCMxLQJnOE6ltHkGr4v+DVi2Df/5peJRlmMuW6ryhM5VAJwyV6i7TesDZVFhpe4VCZ39cb/UAVI0TQrMPoNDZH/9rFYViZ1+EZu2Df/4ZOKklNTqGTK9wXPYfDaPWDe1vHYFn6U24Gm/BoHVHrnsneBdfxW23znApz0Oad19kefWutl5HKy4uxqRJk5CXlwcvL/v3/TW7JMnWSFJwcDCys7MrBaK0tBTXr19HaGgoXFwcf7ITQqCgoACenp5QfsMqOtOnT8enn8q/qjg5OSEkJARTpkzBokWLoNPV7+Dg9OnTkZubi+3bt9eo/NWrV9G5c2ckJCTg/vvvr9e2WOrfvz/mzJmDyZPvrJhUVXxPnz6NpUuX4siRI8jOzkZoaCief/55vPzyy+YyBoMBnTt3xueff47Bgwfb/N7S0lJcvXoVwcHBDdJnmhKj0Yg9e/Zg+PDhcHKqwbQDqhXG17EYX8difB3P4TEuK5D3W5nvUVLk/VQ6Zzkl7G5F2dB+Owsi4H6og+fJ+4x0LnL63u2rUIqzIdoPlGXrcs2jlsupf06uQEE6tAf+CuXKD1BKble5m/AOhunJTyCCKk1OrCw/DZrETVBunIDm6o+26/O7DyIwEkpKPESrUAjfHtDcOAFkXoAiTBCtOwFCQLmdIkcNwydCkxAnR5Aq6tDoILqOgOjwANSIyXLpfJMBcGsjC+Rdl1MPq7r3SC0HirKg3EyECIyUU+zKCqA5vhrKjRNQcq/Lzyumgloeg94diqHy9uoIZ0+YRi6DCOoD7bFVUE59BtH791DSz0DJTqq+grvku7SDbvZxODk37vVTfn4+fH19q02SmvR0O19fX2i1WmRkZFhtz8jIQECA7fmXzs7OcHaufEOhk5NTpZOKyWSCoijQaDTQNMA814opYBXfWVeKomDkyJHYsGEDysrKsHPnTsTExECv11tNTaxgMBjqPFKmKEqt2ltRrj5iajQabf6P4PDhw0hOTsb48eOtvqOq+CYmJsLf3x+bN29GcHAwjh49iueeew46nQ6zZ8sHKbq4uGDSpEn48MMP8fDDD9s9PkVRbPanlqIlH3tDYHwdi/F1LMbX8RwWY6fWgEfr6stV8AkE/vA1AKDSfI6AnvXRIAC/jp61Dgae+vW+srICIHm/vH8JkM/Xun7nj+ZK3nXoNkQDnR4BhrwJtOkCaPUy2VIUed+QRiuf4/XvmeYFO4TGCTe8+yFw6CzoNACOrAIGzIQS/oz5D69WqZ4QgKLIbUIAhkIozp4yFoNflfeUOXsBLj5QNBrzvjbnvvh2qlk8nEOA1hbP/tK3AYYtti6WdUkem9998n4w97ZQtPo70+Ku/ywX+UjYcOeB1q6t5YOcjcVydUZjCdBzNJSIidBVLAIybjUw6l0ozp5yFcb/OyiT58Pvy/urbHH2AoJloqyq5TiPPuind270c0RNv79JJ0l6vR59+/bFvn37MG7cOADyQnjfvn3mC9t6JUT1q8/8Fqoq6zdoK9986ORWq7+0ODs7mxPFWbNmYfv27fj222+xaNEiTJs2Dbm5uejfvz9Wr14NZ2dnpKSk4Pr165g3bx5++OEHaDQaDB48GCtXrkRoaCgAmTTOnz8fcXFx0Gq1mDFjBu4eaNy1axeWLFmCc+fOQavVIioqCitXrkTnznJudceOcp5rZKR8PsbDDz+M+Ph4qKqKJUuWYP369cjKykLPnj3x7rvvYuTIkQDkCFTHjh2xZcsW/POf/8Tx48exdu1aTJs2rdKxb9myBcOHDzeP5AghMHz4cGg0GmzduhUAcOvWLYSHh+PZZ5/FX/7yFzz77LNWdXTq1AnHjh3DV199ZdWXRo8ejeHDh6OkpASurlxdh4iIqElx9gTuG3vnfVTMnZ8LMoB/z5D37/xfvHxVCIyQCdP57XIkx1L4BJT3fw4nT97AY90fA5ycrL/DFstrNkWR7bJ83yq0lgdWT9p2ky97gvvL16il1sdQlC0XaGjVwf6+Fceocwa6Rcufuw6Ti278L0EmW26t5WIeGp285nWWC3uYjEZk7tx5Tz2PrEknSQAwd+5cTJ06Ff369cOAAQOwYsUKFBUVmVe7q1fGYuDvQfVf7680AHzsffinm/I5CXXk6uqKnJwc8/t9+/bBy8sLe/bsASBHZUaMGIGoqCgcOnQIOp0OS5YswciRI3HmzBno9XosX74cGzduRFxcHHr27Inly5dj+/btGDJkiLneoqIizJ07F+Hh4SgsLMTixYvxxBNP4NSpU9BoNDhx4gQGDBiAvXv3olevXuYRrJUrV2L58uVYt24dIiMjERcXhzFjxuD8+fPo2rWruf6FCxdi+fLliIyMtDud7dChQ5g0aZL5vaIo2LRpE8LCwrBu3Tq8/vrreOGFF9CuXTssXrzYZh0AkJeXh9atrf9q1q9fP5SXl+P48eN45JFHav4LICIiosbl6Q9M3gYcXwscXw+U5t1ZiTDttHxV0DoDwQOAh16To05GI4AbjdHqxnF3suLuK191odHKWN7t1wTpXtXkk6RnnnkGWVlZWLx4MdLT03H//fdj165d8Pf3b+ymNQlCCOzbtw+7d+/GSy+9ZN7u7u6Ojz/+2JykbN68Gaqq4uOPPzYPG2/YsAE+Pj6Ij49HdHQ0VqxYgUWLFuHJJ58EIJdb3717t9X3PfXUU1bv4+Li0LZtW1y4cAG9e/dG27ZtAQBt2rSxmhL53nvvYcGCBZgwYQIAYOnSpThw4ABWrFiB1atXm8vNmTPH/P32pKamIijIOplt164d1qxZYx5F27lzJxITE+3eo3X06FFs3boVO3bssNru5uYGb29vpKamVtkGIiIiaoKcXIEHX5UvAEg/B6SfkVPyim/JpeHb9gD8e1k/T4roLk0+SQKA2bNnO2Z63d2c3OSIjoOoqor8ggJ4eXpWvl+nlktZfv/99/Dw8IDRaISqqpg0aRLeeust8+dhYWFW9yGdPn0aV65cgaenp1U9paWlSE5ORl5eHtLS0jBw4EDzZzqdDv369bOacnf58mUsXrwYx48fR3Z2tvk+oGvXrqF3b9srluTn5+PmzZt44IEHrLY/8MADOH36tNW2fv36VXvsJSUlNkeZxo8fj23btmHp0qVYs2aN1QiVpXPnzmHs2LGIjY1FdHR0pc9dXV1RXOzAaZdERETUMAJ6y9f9k6ovS2ThnkiSGoyi/KYpb9VSVcDJJL/jNy5q8Oijj2LNmjXQ6/UICgqqNGLi7m59HIWFhejbt6/NpbwrRn9qYvTo0ejQoQM++ugjBAUFQVVV9O7dGwZD5SUo6+Ludtvi6+uL27crr25TXFyM06dPQ6vV4vLlyzb3vXDhAoYOHYrnnnsOb7zxhs0yt27dqlVMiIiIiKh5aYRHF1N9cHd3R5cuXRASElKjZb/79OmDy5cvw8/PD126dLF6eXt7w9vbG4GBgVbLqpeXlyMhIcH8PicnB0lJSXjjjTcwdOhQ9OzZs1KyUjF6ZTLdecaAl5cXgoKCcOSI9eonR44cwX333VfrY4+MjMSFCxcqbX/ttdeg0WiwY8cOrFq1Cvv377f6/Pz583j00UcxdepU/O1vf7NZd3JyMkpLS80LTxARERFRy8MkqYWYPHkyfH19MXbsWBw6dAgpKSmIj4/Hyy+/jBs35I2Kr7zyCt599118/fXX+O9//4sXX3wRubm55jpatWqFNm3aYP369bhy5Qr279+PuXPnWn2Pn58fXF1dsWvXLmRkZCAvLw8AMH/+fCxduhRbt25FUlISFi5ciFOnTuGVV16p9bGMGDEChw8fttq2Y8cObNiwAevXr8fw4cMxf/58TJ061ZzEnTt3Do8++iiio6Mxd+5cpKenIz09HVlZWVb1HDp0CJ06dTKv1kdERERELQ+TpBbCzc0NP/74I0JCQvDkk0+iZ8+emDFjBkpLS80P0po3bx6mTJmCqVOnIioqCp6ennjiiSfMdWg0GmzZsgUJCQno3bs3Xn31VSxbtszqe3Q6HVatWoV169YhKCgIY8fKJTRffvllzJ07F/PmzUNYWBh27dqFb7/91u59Q1WZPHkyzp8/j6Qk+SCzrKwszJgxA7GxsYiIiAAAvP322/D398cLL7wAAPjXv/6FrKwsbN68GYGBgeZX//79rer+4osvMHPmzFq3iYiIiIiaD0Xc/SCcZiY/Px/e3t42n6pbWlqKlJQUdOzY0e5y0/VJVVXk5+fDy8urQR5e25zNnz8f+fn5WLdunXnbb43v+fPnMWTIEFy6dAne3t42yzR0n2lKjEYjdu7ciccee6zRHwTXHDG+jsX4Ohbj63iMsWMxvo7VlOJbVW5giVfqdE/685//jA4dOphX16sPaWlp+PTTT+0mSERERETUMnB1O7on+fj44E9/+lO91jls2LB6rY+IiIiI7k0cSSIiIiIiIrLAJImIiIiIiMgCkyQAzXztCqpH7CtEREREzV+LTpIqVtcoLi5u5JbQvcJgMAAAtFptI7eEiIiIiBylRS/coNVq4ePjg8zMTADyWUKKojjs+1RVhcFgQGlpKZcAdwBHx1dVVWRlZcHNzQ06XYv+p0NERETUrLX4K72AgAAAMCdKjiSEQElJCVxdXR2ajLVUDRFfjUaDkJAQ/v6IiIiImrEWnyQpioLAwED4+fnBaDQ69LuMRiN+/PFHPPTQQ43+IK3mqCHiq9frOQpIRERE1My1+CSpglardfh9JlqtFuXl5XBxcWGS5ACMLxERERHVB/5JnIiIiIiIyAKTJCIiIiIiIgtMkoiIiIiIiCw0+3uSKh7+mZ+f38gtkQsLFBcXIz8/n/fMOADj61iMr2Mxvo7F+DoW4+t4jLFjMb6O1ZTiW5ETVOQI9jT7JKmgoAAAEBwc3MgtISIiIiKipqCgoADe3t52P1dEdWnUPU5VVdy8eROenp6N/myb/Px8BAcH4/r16/Dy8mrUtjRHjK9jMb6Oxfg6FuPrWIyv4zHGjsX4OlZTiq8QAgUFBQgKCqrysS7NfiRJo9Ggffv2jd0MK15eXo3eQZozxtexGF/HYnwdi/F1LMbX8Rhjx2J8HaupxLeqEaQKXLiBiIiIiIjIApMkIiIiIiIiC0ySGpCzszNiY2Ph7Ozc2E1plhhfx2J8HYvxdSzG17EYX8djjB2L8XWsezG+zX7hBiIiIiIiotrgSBIREREREZEFJklEREREREQWmCQRERERERFZYJJERERERERkgUlSPVu9ejVCQ0Ph4uKCgQMH4sSJE1WW37ZtG3r06AEXFxeEhYVh586dDdTSe8s777yD/v37w9PTE35+fhg3bhySkpKq3Gfjxo1QFMXq5eLi0kAtvre89dZblWLVo0ePKvdh362d0NDQSjFWFAUxMTE2y7P/Vu3HH3/E6NGjERQUBEVR8PXXX1t9LoTA4sWLERgYCFdXVwwbNgyXL1+utt7ansObq6riazQasWDBAoSFhcHd3R1BQUH4wx/+gJs3b1ZZZ13OM81Vdf132rRplWI1cuTIautl/5Wqi6+tc7GiKFi2bJndOtl/pZpcj5WWliImJgZt2rSBh4cHnnrqKWRkZFRZb13P2Y7EJKkebd26FXPnzkVsbCxOnjyJiIgIjBgxApmZmTbLHz16FBMnTsSMGTOQmJiIcePGYdy4cTh37lwDt7zpO3jwIGJiYvDTTz9hz549MBqNiI6ORlFRUZX7eXl5IS0tzfxKTU1toBbfe3r16mUVq8OHD9sty75bez///LNVfPfs2QMAGD9+vN192H/tKyoqQkREBFavXm3z83/84x9YtWoV1q5di+PHj8Pd3R0jRoxAaWmp3Tprew5vzqqKb3FxMU6ePIk333wTJ0+exFdffYWkpCSMGTOm2nprc55pzqrrvwAwcuRIq1h98cUXVdbJ/ntHdfG1jGtaWhri4uKgKAqeeuqpKutl/63Z9dirr76K7777Dtu2bcPBgwdx8+ZNPPnkk1XWW5dztsMJqjcDBgwQMTEx5vcmk0kEBQWJd955x2b5p59+Wjz++ONW2wYOHCief/55h7azOcjMzBQAxMGDB+2W2bBhg/D29m64Rt3DYmNjRURERI3Ls+/+dq+88oro3LmzUFXV5ufsvzUHQGzfvt38XlVVERAQIJYtW2belpubK5ydncUXX3xht57ansNbirvja8uJEycEAJGammq3TG3PMy2FrfhOnTpVjB07tlb1sP/aVpP+O3bsWDFkyJAqy7D/2nb39Vhubq5wcnIS27ZtM5e5ePGiACCOHTtms466nrMdjSNJ9cRgMCAhIQHDhg0zb9NoNBg2bBiOHTtmc59jx45ZlQeAESNG2C1Pd+Tl5QEAWrduXWW5wsJCdOjQAcHBwRg7dizOnz/fEM27J12+fBlBQUHo1KkTJk+ejGvXrtkty7772xgMBmzevBnPPvssFEWxW479t25SUlKQnp5u1Ue9vb0xcOBAu320LudwuiMvLw+KosDHx6fKcrU5z7R08fHx8PPzQ/fu3TFr1izk5OTYLcv+W3cZGRnYsWMHZsyYUW1Z9t/K7r4eS0hIgNFotOqLPXr0QEhIiN2+WJdzdkNgklRPsrOzYTKZ4O/vb7Xd398f6enpNvdJT0+vVXmSVFXFnDlz8MADD6B37952y3Xv3h1xcXH45ptvsHnzZqiqikGDBuHGjRsN2Np7w8CBA7Fx40bs2rULa9asQUpKCgYPHoyCggKb5dl3f5uvv/4aubm5mDZtmt0y7L91V9EPa9NH63IOJ6m0tBQLFizAxIkT4eXlZbdcbc8zLdnIkSPx6aefYt++fVi6dCkOHjyIUaNGwWQy2SzP/lt3mzZtgqenZ7XTwdh/K7N1PZaeng69Xl/pDybVXQ9XlKnpPg1B12jfTFRHMTExOHfuXLVzgaOiohAVFWV+P2jQIPTs2RPr1q3DX//6V0c3854yatQo88/h4eEYOHAgOnTogC+//LJGf12j2vnkk08watQoBAUF2S3D/kv3AqPRiKeffhpCCKxZs6bKsjzP1NyECRPMP4eFhSE8PBydO3dGfHw8hg4d2ogta37i4uIwefLkahfGYf+trKbXY/cqjiTVE19fX2i12kqrd2RkZCAgIMDmPgEBAbUqT8Ds2bPx/fff48CBA2jfvn2t9nVyckJkZCSuXLnioNY1Hz4+PujWrZvdWLHv1l1qair27t2LP/7xj7Xaj/235ir6YW36aF3O4S1dRYKUmpqKPXv2VDmKZEt15xm6o1OnTvD19bUbK/bfujl06BCSkpJqfT4G2H/tXY8FBATAYDAgNzfXqnx118MVZWq6T0NgklRP9Ho9+vbti3379pm3qaqKffv2Wf012FJUVJRVeQDYs2eP3fItmRACs2fPxvbt27F//3507Nix1nWYTCacPXsWgYGBDmhh81JYWIjk5GS7sWLfrbsNGzbAz88Pjz/+eK32Y/+tuY4dOyIgIMCqj+bn5+P48eN2+2hdzuEtWUWCdPnyZezduxdt2rSpdR3VnWfojhs3biAnJ8durNh/6+aTTz5B3759ERERUet9W2r/re56rG/fvnBycrLqi0lJSbh27ZrdvliXc3aDaLQlI5qhLVu2CGdnZ7Fx40Zx4cIF8dxzzwkfHx+Rnp4uhBBiypQpYuHChebyR44cETqdTrz33nvi4sWLIjY2Vjg5OYmzZ8821iE0WbNmzRLe3t4iPj5epKWlmV/FxcXmMnfH9+233xa7d+8WycnJIiEhQUyYMEG4uLiI8+fPN8YhNGnz5s0T8fHxIiUlRRw5ckQMGzZM+Pr6iszMTCEE+259MZlMIiQkRCxYsKDSZ+y/tVNQUCASExNFYmKiACDef/99kZiYaF5d7d133xU+Pj7im2++EWfOnBFjx44VHTt2FCUlJeY6hgwZIj744APz++rO4S1JVfE1GAxizJgxon379uLUqVNW5+SysjJzHXfHt7rzTEtSVXwLCgrEa6+9Jo4dOyZSUlLE3r17RZ8+fUTXrl1FaWmpuQ72X/uqOz8IIUReXp5wc3MTa9assVkH+69tNbkee+GFF0RISIjYv3+/+OWXX0RUVJSIioqyqqd79+7iq6++Mr+vyTm7oTFJqmcffPCBCAkJEXq9XgwYMED89NNP5s8efvhhMXXqVKvyX375pejWrZvQ6/WiV69eYseOHQ3c4nsDAJuvDRs2mMvcHd85c+aYfxf+/v7iscceEydPnmz4xt8DnnnmGREYGCj0er1o166deOaZZ8SVK1fMn7Pv1o/du3cLACIpKanSZ+y/tXPgwAGb54SKGKqqKt58803h7+8vnJ2dxdChQyvFvUOHDiI2NtZqW1Xn8JakqvimpKTYPScfOHDAXMfd8a3uPNOSVBXf4uJiER0dLdq2bSucnJxEhw4dxMyZMyslO+y/9lV3fhBCiHXr1glXV1eRm5trsw72X9tqcj1WUlIiXnzxRdGqVSvh5uYmnnjiCZGWllapHst9anLObmiKEEI4ZoyKiIiIiIjo3sN7koiIiIiIiCwwSSIiIiIiIrLAJImIiIiIiMgCkyQiIiIiIiILTJKIiIiIiIgsMEkiIiIiIiKywCSJiIiIiIjIApMkIiIiIiIiC0ySiIioWZg2bRrGjRvX2M0gIqJmQNfYDSAiIqqOoihVfh4bG4uVK1dCCNFALSIiouaMSRIRETV5aWlp5p+3bt2KxYsXIykpybzNw8MDHh4ejdE0IiJqhjjdjoiImryAgADzy9vbG4qiWG3z8PCoNN3ukUcewUsvvYQ5c+agVatW8Pf3x0cffYSioiJMnz4dnp6e6NKlC/7zn/9Yfde5c+cwatQoeHh4wN/fH1OmTEF2dnYDHzERETUmJklERNRsbdq0Cb6+vjhx4gReeuklzJo1C+PHj8egQYNw8uRJREdHY8qUKSguLgYA5ObmYsiQIYiMjMQvv/yCXbt2ISMjA08//XQjHwkRETUkJklERNRsRURE4I033kDXrl2xaNEiuLi4wNfXFzNnzkTXrl2xePFi5OTk4MyZMwCADz/8EJGRkfj73/+OHj16IDIyEnFxcThw4AAuXbrUyEdDREQNhfckERFRsxUeHm7+WavVok2bNggLCzNv8/f3BwBkZmYCAE6fPo0DBw7YvL8pOTkZ3bp1c3CLiYioKWCSREREzZaTk5PVe0VRrLZVrJqnqioAoLCwEKNHj8bSpUsr1RUYGOjAlhIRUVPCJImIiOhXffr0wb///W+EhoZCp+P/IomIWirek0RERPSrmJgY3Lp1CxMnTsTPP/+M5ORk7N69G9OnT4fJZGrs5hERUQNhkkRERPSroKAgHDlyBCaTCdHR0QgLC8OcOXPg4+MDjYb/yyQiaikUwceTExERERERmfHPYkRERERERBaYJBEREREREVlgkkRERERERGSBSRIREREREZEFJklEREREREQWmCQRERERERFZYJJERERERERkgUkSERERERGRBSZJREREREREFpgkERERERERWWCSREREREREZOH/AwzGIifMBazLAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the results\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(t_span, data_stochastic_hat[:, 0,0], label='Prey (x1)')\n",
    "plt.plot(t_span, data_stochastic_hat[:,0, 1], label='Predator (x2)')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Population')\n",
    "plt.title('Learned Stochastic Lotka-Volterra Trajectories')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is clear using our rollout-based loss functions on observed stochastic data, similar to that in the Neural ODE case, does not work as it cannot capture the inherent randomness. Another way of putting it is the undeterministic nature of the system can cause the learned system to be unstable and blow up. Developing appropriate loss functions to ensure correctness and stability using this rollout framework is an open research question. Thus we necessitate the need to reformulate the above procedure + loss in a different way. We do so in a latent space and use a variational inference method to learn the stochastic data-generating distributions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Latent SDEs\n",
    "\n",
    "Using mean squared error (MSE) to train a neural SDE on time pairs might encounter challenges due to the stochastic nature of SDEs. While MSE is a common loss function used for deterministic systems, it may not be directly applicable to stochastic systems like SDEs. As discussed above, this is because these rollout-MSE -based losses do not capture the underlying uncertainty: \n",
    "\n",
    "* Ignoring Stochasticity: MSE only considers the deterministic part of the model and ignores the stochastic component represented by the diffusion term in the SDE. This can lead to suboptimal results as the model does not capture the inherent randomness in the system.\n",
    "\n",
    "* Overfitting the Drift Term: MSE optimization might focus excessively on minimizing the errors in the drift term while neglecting the diffusion term. This can result in overfitting of the deterministic part of the model and underfitting of the stochastic part.\n",
    "\n",
    "SDEs inherently involve randomness or uncertainty, typically represented by the stochastic terms in the differential equations. Instead of using refernence tracking + finite difference loss through our rollout mechanism, we can train the model using variational inference. \n",
    "\n",
    "#### Variaional Inference \n",
    "Variational inference allows us to capture this uncertainty by providing a probabilistic characterization of the latent variables' distribution. Instead of obtaining a single point estimate, variational inference provides a full probabilistic description, including measures of uncertainty such as confidence intervals or predictive distributions. Variational inference offers a solution to this problem by approximating the true posterior distribution with a simpler, parameterized distribution, often chosen from a family of distributions such as Gaussian distributions. This is done via an encoder network. The decoder network draws from samples of this learned, approximate posterior to reconstruct the data distribution. Using the KL divergence, between these distributions, we learn the latent space's parameters known as the variational parameters. \n",
    "\n",
    "The Latent SDE is essentially a variational \"autoencoder\" where instead of seeking resynthesize samples corresponding to the \"same time\" instance, it tries to reconstruct future samples given the current dynamics of the system, where the dynamics are known to be modeled via a SDE. To do this, the latent space is **itself** going to be governed via an SDE and we perform integration on the latent space to synthesize forward-looking samples. \n",
    "\n",
    "#### Latent SDE Architecture\n",
    "\n",
    "We now define the architecture for the Latent SDE and train on a 3D stochastic Lorenz attractor dataset. Code adapted from https://github.com/google-research/torchsde/blob/master/examples/latent_sde_lorenz.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. **Encoder**:\n",
    "   - The encoder maps each input data point $x$ to a distribution over latent variables $z$. This distribution is typically Gaussian with mean $\\mu$ and standard deviation $\\sigma$.\n",
    "   $$\n",
    "   q_{\\phi}(z | x) = \\mathcal{N}(\\mu_{\\phi}(x), \\sigma_{\\phi}(x))\n",
    "   $$\n",
    "   - Here, $\\mu_{\\phi}(x)$ and $\\sigma_{\\phi}(x)$ are the mean and standard deviation parameters of the Gaussian distribution, which are output by the encoder neural network parameterized by $\\phi$.\n",
    "\n",
    "2. **Latent Dynamics**:\n",
    "   - The latent variables $z$ evolve over time according to a stochastic differential equation (SDE). The dynamics of $z$ are governed by drift and diffusion functions, similar to the SDE for the observed data $x$.\n",
    "   $$\n",
    "   dz_t = f(z_t, t) \\, dt + G(z_t, t) \\, dW_t\n",
    "   $$\n",
    "   - $f(z_t, t)$ represents the drift component, determining the deterministic evolution of the latent variables.\n",
    "   - $G(z_t, t)$ represents the diffusion component, introducing stochasticity into the latent dynamics.\n",
    "   - $dW_t$ is the increment of a Wiener process (Brownian motion), representing random noise.\n",
    "\n",
    "3. **Decoder**:\n",
    "   - The decoder takes samples from the latent space $z$ and maps them back to the data space $x$. It models the conditional distribution of $x$ given $z$.\n",
    "   $$\n",
    "   p_{\\theta}(x | z)\n",
    "   $$\n",
    "   - The decoder neural network, parameterized by $\\theta$, outputs the parameters of the conditional distribution $p_{\\theta}(x | z)$, such as the mean and variance of a Gaussian distribution or the parameters of a Bernoulli distribution for binary data.\n",
    "\n",
    "4. **Latent Variable Prior**:\n",
    "   - We assume a prior distribution over the latent variables $z$. This distribution is typically chosen to be a standard Gaussian.\n",
    "   $$\n",
    "   p(z) = \\mathcal{N}(0, I)\n",
    "   $$\n",
    "\n",
    "      though in TorchSDE's framework (and as shown in the code below), these are learnable parameters pz0_mean and pz0_logstd\n",
    "\n",
    "5. **Objective Function**:\n",
    "   - The objective function for training the Latent SDE model is similar to the ELBO in VAEs but now includes the evolution of latent variables governed by the SDE.\n",
    "   $$\n",
    "   \\text{ELBO}(\\theta, \\phi; x) = \\mathbb{E}_{q_{\\phi}(z | x)} [\\log p_{\\theta}(x | z)] - \\text{KL}[q_{\\phi}(z | x) || p(z)]\n",
    "   $$\n",
    "   - The first term represents the reconstruction loss, measuring how well the decoder reconstructs the input data $x$ from the latent variable samples $z$.\n",
    "   - The second term is the KL divergence between the approximate posterior $q_{\\phi}(z | x)$ and the prior $p(z)$, which encourages the approximate posterior to match the prior.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Functions to generate data from a Lorenz attractor\n",
    "\n",
    "We define the 3D stochastic Lorenz attractor model. This model as appropriate $f$ and $g$ functions defined. To produce data samples we integrate this using the Euler-Murayama integrator from TorchSDE library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "class StochasticLorenzAttractor(BaseSDESystem):\n",
    "    def __init__(self, a: Sequence = (10., 28., 8 / 3), b: Sequence = (.1, .28, .3)):\n",
    "        super().__init__()\n",
    "        self.a = a\n",
    "        self.b = b\n",
    "\n",
    "    def f(self, t, y):\n",
    "        x1, x2, x3 = torch.split(y, split_size_or_sections=(1, 1, 1), dim=1)\n",
    "        a1, a2, a3 = self.a\n",
    "\n",
    "        f1 = a1 * (x2 - x1)\n",
    "        f2 = a2 * x1 - x2 - x1 * x3\n",
    "        f3 = x1 * x2 - a3 * x3\n",
    "        return torch.cat([f1, f2, f3], dim=1)\n",
    "\n",
    "    def g(self, t, y):\n",
    "        x1, x2, x3 = torch.split(y, split_size_or_sections=(1, 1, 1), dim=1)\n",
    "        b1, b2, b3 = self.b\n",
    "\n",
    "        g1 = x1 * b1\n",
    "        g2 = x2 * b2\n",
    "        g3 = x3 * b3\n",
    "        return torch.cat([g1, g2, g3], dim=1)\n",
    "    \n",
    "    @torch.no_grad()\n",
    "    def sample(self, x0, ts, noise_std, normalize):\n",
    "        \"\"\"Sample data for training. Store data normalization constants if necessary.\"\"\"\n",
    "        xs = torchsde.sdeint(self, x0, ts)\n",
    "        if normalize:\n",
    "            mean, std = torch.mean(xs, dim=(0, 1)), torch.std(xs, dim=(0, 1))\n",
    "            xs.sub_(mean).div_(std).add_(torch.randn_like(xs) * noise_std)\n",
    "        return xs\n",
    "stochastic_lorenz_model = StochasticLorenzAttractor()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Neuromancer Integration\n",
    "\n",
    "As per the NeuroMANCER x Lightning workflow, generate the data_setup_function and return the DictDatasets. Note that we only have a train dataset here, so we return `None` for dev/test datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_dataset(t0, t1, batch_size, noise_std, steps=100):\n",
    "    _y0 = torch.randn(batch_size, 3)\n",
    "    ts = torch.linspace(t0, t1, steps=steps)\n",
    "    xs = stochastic_lorenz_model.sample(_y0, ts, noise_std, normalize=True)\n",
    "    train_data = DictDataset({'xs':xs},name='train')\n",
    "    return train_data, None, None, batch_size\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define some experimental parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_size = 3\n",
    "batch_size=1024\n",
    "latent_size=4\n",
    "context_size=64\n",
    "hidden_size=128\n",
    "lr_init=1e-2\n",
    "t0=0.\n",
    "t1=2.\n",
    "lr_gamma=0.997\n",
    "num_iters=1\n",
    "kl_anneal_iters=1000\n",
    "pause_every=50\n",
    "noise_std=0.01\n",
    "method=\"euler\"\n",
    "steps = 100 # number of time steps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the aforementioned discussion as well as equations above for the latent SDE, we now define the model architecture consisting of a LatentSDE Encoder and Decoder. For integration in the latent space we use a LatentSDE Integrator."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Encoder(nn.Module):\n",
    "    \"\"\"\n",
    "    Encoder module to handle time-series data (as in the case of stochastic data and SDE)\n",
    "    GRU is used to handle mapping to latent space in this case\n",
    "    This class is used only in LatentSDE_Encoder\n",
    "    \"\"\"\n",
    "    def __init__(self, input_size, hidden_size, output_size):\n",
    "        super().__init__()\n",
    "        self.gru = nn.GRU(input_size=input_size, hidden_size=hidden_size)\n",
    "        self.lin = nn.Linear(hidden_size, output_size)\n",
    "\n",
    "    def forward(self, inp):\n",
    "        out, _ = self.gru(inp)\n",
    "        out = self.lin(out)\n",
    "        return out\n",
    "    \n",
    "class LatentSDE_Encoder(BaseSDESystem):\n",
    "    def __init__(self, data_size, latent_size, context_size, hidden_size, ts, adjoint=False):\n",
    "        \"\"\"\n",
    "        LatentSDE_Encoder is a neural network module designed for encoding time-series data into a latent space representation,\n",
    "        which is then used to model the system dynamics using Stochastic Differential Equations (SDEs).\n",
    "\n",
    "        The primary purpose of this class is to transform high-dimensional time-series data into a lower-dimensional latent space\n",
    "        while capturing the underlying stochastic dynamics. This transformation facilitates efficient modeling, prediction, and\n",
    "        inference of complex temporal processes. \n",
    "\n",
    "        Taken from https://github.com/google-research/torchsde/blob/master/examples/latent_sde_lorenz.py and modified to support \n",
    "        NeuroMANCER library\n",
    "\n",
    "        :param data_size: (int) state size of the data \n",
    "        :param latent_size: (int) input latent size for the encoder \n",
    "        :param context_size: (int) size of context vector (output of encoder)\n",
    "        :param hidden_size: (int) size of the hidden layer of encoder \n",
    "        :param ts: (tensor) tensor of timesteps over which data should be predicted\n",
    "\n",
    "        \"\"\"\n",
    "        super().__init__()\n",
    "\n",
    "        self.adjoint = adjoint\n",
    "\n",
    "        # Encoder.\n",
    "        self.encoder = Encoder(input_size=data_size, hidden_size=hidden_size, output_size=context_size)\n",
    "        self.qz0_net = nn.Linear(context_size, latent_size + latent_size) #Layer to return mean and variance of the parameterized latent space\n",
    "\n",
    "        # Decoder.\n",
    "        self.f_net = nn.Sequential(\n",
    "            nn.Linear(latent_size + context_size, hidden_size),\n",
    "            nn.Softplus(),\n",
    "            nn.Linear(hidden_size, hidden_size),\n",
    "            nn.Softplus(),\n",
    "            nn.Linear(hidden_size, latent_size),\n",
    "        )\n",
    "        self.h_net = nn.Sequential(\n",
    "            nn.Linear(latent_size, hidden_size),\n",
    "            nn.Softplus(),\n",
    "            nn.Linear(hidden_size, hidden_size),\n",
    "            nn.Softplus(),\n",
    "            nn.Linear(hidden_size, latent_size),\n",
    "        )\n",
    "        # This needs to be an element-wise function for the SDE to satisfy diagonal noise.\n",
    "        self.g_nets = nn.ModuleList(\n",
    "            [\n",
    "                nn.Sequential(\n",
    "                    nn.Linear(1, hidden_size),\n",
    "                    nn.Softplus(),\n",
    "                    nn.Linear(hidden_size, 1),\n",
    "                    nn.Sigmoid()\n",
    "                )\n",
    "                for _ in range(latent_size)\n",
    "            ]\n",
    "        )\n",
    "        self.projector = nn.Linear(latent_size, data_size)\n",
    "\n",
    "        self.pz0_mean = nn.Parameter(torch.zeros(1, latent_size))\n",
    "        self.pz0_logstd = nn.Parameter(torch.zeros(1, latent_size))\n",
    "\n",
    "        self._ctx = None\n",
    "        self.ts = ts\n",
    "\n",
    "    def contextualize(self, ctx):\n",
    "        self._ctx = ctx  # A tuple of tensors of sizes (T,), (T, batch_size, d).\n",
    "\n",
    "    def f(self, t, y):\n",
    "        ts, ctx = self._ctx\n",
    "\n",
    "        i = min(torch.searchsorted(ts, t, right=True), len(ts) - 1)\n",
    "\n",
    "        return self.f_net(torch.cat((y, ctx[i]), dim=1))\n",
    "\n",
    "    def h(self, t, y):\n",
    "        return self.h_net(y)\n",
    "\n",
    "    def g(self, t, y):  # Diagonal diffusion.\n",
    "        y = torch.split(y, split_size_or_sections=1, dim=1)\n",
    "        out = [g_net_i(y_i) for (g_net_i, y_i) in zip(self.g_nets, y)]\n",
    "        return torch.cat(out, dim=1)\n",
    "\n",
    "    def forward(self, xs):\n",
    "        # Contextualization is only needed for posterior inference.\n",
    "        ctx = self.encoder(torch.flip(xs, dims=(0,)))\n",
    "        ctx = torch.flip(ctx, dims=(0,))\n",
    "        self.contextualize((self.ts, ctx))\n",
    "\n",
    "        qz0_mean, qz0_logstd = self.qz0_net(ctx[0]).chunk(chunks=2, dim=1)\n",
    "        z0 = qz0_mean + qz0_logstd.exp() * torch.randn_like(qz0_mean)\n",
    "        if not self.adjoint:\n",
    "            return z0, xs, self.ts, qz0_mean, qz0_logstd\n",
    "        else:\n",
    "            adjoint_params = (\n",
    "                    (ctx,) +\n",
    "                    tuple(self.f_net.parameters()) + tuple(self.g_nets.parameters()) + tuple(self.h_net.parameters())\n",
    "            )\n",
    "            return z0, xs, self.ts, qz0_mean, qz0_logstd, adjoint_params\n",
    "\n",
    "class LatentSDE_Decoder(BaseSDESystem):\n",
    "    \"\"\"\n",
    "    Second part of Wrapper for torchsde's Latent SDE class to integrate with Neuromancer. This takes in output of\n",
    "    LatentSDEIntegrator and decodes it back into the \"real\" data space and also outputs associated Gaussian distributions\n",
    "    to be used in the final loss function.\n",
    "    Please see https://github.com/google-research/torchsde/blob/master/examples/latent_sde_lorenz.py\n",
    "\n",
    "    :param data_size: (int) state size of the data \n",
    "    :param latent_size: (int) input latent size for the encoder \n",
    "    :param noise_std: (float) standard deviation of the Gaussian noise applied during decoding\n",
    "    \"\"\"\n",
    "    def __init__(self, data_size, latent_size, noise_std):\n",
    "        super().__init__()\n",
    "        self.noise_std = noise_std\n",
    "        self.pz0_mean = nn.Parameter(torch.zeros(1, latent_size))\n",
    "        self.pz0_logstd = nn.Parameter(torch.zeros(1, latent_size))\n",
    "        self.projector = nn.Linear(latent_size, data_size)\n",
    "    \n",
    "    def f(self, t, y): \n",
    "        pass #unused \n",
    "    \n",
    "    def g(self, t, y): \n",
    "        pass #unused\n",
    "\n",
    "    def forward(self, xs, zs, log_ratio, qz0_mean, qz0_logstd):\n",
    "        _xs = self.projector(zs)\n",
    "        xs_dist = Normal(loc=_xs, scale=self.noise_std)\n",
    "        log_pxs = xs_dist.log_prob(xs).sum(dim=(0, 2)).mean(dim=0)\n",
    "\n",
    "        qz0 = torch.distributions.Normal(loc=qz0_mean, scale=qz0_logstd.exp())\n",
    "        pz0 = torch.distributions.Normal(loc=self.pz0_mean, scale=self.pz0_logstd.exp())\n",
    "        logqp0 = torch.distributions.kl_divergence(qz0, pz0).sum(dim=1).mean(dim=0)\n",
    "        logqp_path = log_ratio.sum(dim=0).mean(dim=0)\n",
    "        return _xs, log_pxs, logqp0 + logqp_path, log_ratio"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define Neuromancer components, variables, and problem to train the LatentSDE. We define two nodes -- one for encoding and integrating, one for decoding. Upon training, this LatentSDE will generate new samples that exhibit the behavior of the Lorenz attractor training data. For this example, we set `adjoint` to `False` (do not use the adjoint sensitivity method). This is because this method seems to be significantly slower. \n",
    "\n",
    "Also note that we need to pass in the timestep tensor to our `LatentSDE_Encoder`, and as a result need to also define it outside the `make_dataset()` function. We note that this is not the cleanest code and breaks the data abstraction. Additional features will be added to mitigate this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "ts = torch.linspace(t0, t1, steps=steps)\n",
    "\n",
    "sde_encoder = LatentSDE_Encoder(data_size, latent_size, context_size, hidden_size, ts=ts, adjoint=False) \n",
    "integrator = integrators.LatentSDEIntegrator(sde_encoder, adjoint=False)\n",
    "model_1 = Node(integrator, input_keys=['xs'], output_keys=['zs', 'z0', 'log_ratio',  'xs', 'qz0_mean', 'qz0_logstd'], name='m1')\n",
    "sde_decoder = LatentSDE_Decoder(3, latent_size, noise_std=noise_std)\n",
    "model_2 = Node(sde_decoder, input_keys=['xs', 'zs', 'log_ratio', 'qz0_mean', 'qz0_logstd'], output_keys=['xs_hat', 'log_pxs', 'sum_term', 'log_ratio'], name='m2' )\n",
    "\n",
    "\n",
    "\n",
    "log_ratio = variable('log_ratio')\n",
    "log_pxs = variable('log_pxs')\n",
    "\n",
    "# NeuroMANCER loss function format\n",
    "loss = (-1.0*log_pxs + log_ratio) == 0.0\n",
    "\n",
    "# aggregate list of objective terms and constraints\n",
    "objectives = [loss]\n",
    "constraints = []\n",
    "# create constrained optimization loss\n",
    "loss = PenaltyLoss(objectives, constraints)\n",
    "# construct constrained optimization problem\n",
    "problem = Problem([model_1, model_2], loss)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now define helper visualization function (again see https://github.com/google-research/torchsde/blob/master/examples/latent_sde_lorenz.py) that will fire every N epochs in our training loop. This visualization will allow us to see the learned Lorenz attractor samples "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fix the same Brownian motion for visualization.\n",
    "bm_vis = torchsde.BrownianInterval(\n",
    "    t0=t0, t1=t1, size=(batch_size, latent_size,), device='cpu', levy_area_approximation=\"space-time\")\n",
    "\n",
    "# \n",
    "def vis(data_dict, problem, bm_vis, img_path, num_samples=10):\n",
    "    encoder, decoder = problem.nodes[0], problem.nodes[1] #extract the encoder and decoder from our problem\n",
    "\n",
    "    fig = plt.figure(figsize=(20, 9))\n",
    "    gs = gridspec.GridSpec(1, 2)\n",
    "    ax00 = fig.add_subplot(gs[0, 0], projection='3d')\n",
    "    ax01 = fig.add_subplot(gs[0, 1], projection='3d')\n",
    "\n",
    "    xs = data_dict['xs'] #pull out data sample from the DictDataset\n",
    "    # Left plot: data.\n",
    "    z1, z2, z3 = np.split(xs.cpu().numpy(), indices_or_sections=3, axis=-1)\n",
    "    [ax00.plot(z1[:, i, 0], z2[:, i, 0], z3[:, i, 0]) for i in range(num_samples)]\n",
    "    ax00.scatter(z1[0, :num_samples, 0], z2[0, :num_samples, 0], z3[0, :10, 0], marker='x')\n",
    "    ax00.set_yticklabels([])\n",
    "    ax00.set_xticklabels([])\n",
    "    ax00.set_zticklabels([])\n",
    "    ax00.set_xlabel('$z_1$', labelpad=0., fontsize=16)\n",
    "    ax00.set_ylabel('$z_2$', labelpad=.5, fontsize=16)\n",
    "    ax00.set_zlabel('$z_3$', labelpad=0., horizontalalignment='center', fontsize=16)\n",
    "    ax00.set_title('Data', fontsize=20)\n",
    "    xlim = ax00.get_xlim()\n",
    "    ylim = ax00.get_ylim()\n",
    "    zlim = ax00.get_zlim()\n",
    "\n",
    "    # Right plot: samples from learned model.\n",
    "    mydata = data_dict\n",
    "    output = decoder(encoder(mydata))\n",
    "    xs_hat = output['xs_hat'].detach().cpu().numpy() \n",
    "    #xs = latent_sde.sample(batch_size=xs.size(1), ts=ts, bm=bm_vis).cpu().numpy()\n",
    "    z1, z2, z3 = np.split(xs_hat, indices_or_sections=3, axis=-1)\n",
    "\n",
    "    [ax01.plot(z1[:, i, 0], z2[:, i, 0], z3[:, i, 0]) for i in range(num_samples)]\n",
    "    ax01.scatter(z1[0, :num_samples, 0], z2[0, :num_samples, 0], z3[0, :10, 0], marker='x')\n",
    "    ax01.set_yticklabels([])\n",
    "    ax01.set_xticklabels([])\n",
    "    ax01.set_zticklabels([])\n",
    "    ax01.set_xlabel('$z_1$', labelpad=0., fontsize=16)\n",
    "    ax01.set_ylabel('$z_2$', labelpad=.5, fontsize=16)\n",
    "    ax01.set_zlabel('$z_3$', labelpad=0., horizontalalignment='center', fontsize=16)\n",
    "    ax01.set_title('Samples', fontsize=20)\n",
    "    ax01.set_xlim(xlim)\n",
    "    ax01.set_ylim(ylim)\n",
    "    ax01.set_zlim(zlim)\n",
    "\n",
    "    plt.savefig(img_path)\n",
    "    plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Neuromancer training the problem to learn the stochastic process\n",
    "\n",
    "We now train and visualize results using Lightning workflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPU available: False, used: False\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n",
      "/Users/birm560/opt/anaconda3/envs/neuromancer9/lib/python3.10/site-packages/lightning/pytorch/callbacks/model_checkpoint.py:653: Checkpoint directory /Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs exists and is not empty.\n",
      "\n",
      "  | Name    | Type    | Params\n",
      "------------------------------------\n",
      "0 | problem | Problem | 104 K \n",
      "------------------------------------\n",
      "104 K     Trainable params\n",
      "0         Non-trainable params\n",
      "104 K     Total params\n",
      "0.420     Total estimated model params size (MB)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "USING BATCH SIZE  1024\n",
      "USING LEARNING RATE  0.001\n",
      "                                                  "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/birm560/opt/anaconda3/envs/neuromancer9/lib/python3.10/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:441: The 'val_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=15` in the `DataLoader` to improve performance.\n",
      "/Users/birm560/opt/anaconda3/envs/neuromancer9/lib/python3.10/site-packages/lightning/pytorch/utilities/data.py:104: Total length of `DataLoader` across ranks is zero. Please make sure this was your intention.\n",
      "/Users/birm560/opt/anaconda3/envs/neuromancer9/lib/python3.10/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:441: The 'train_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=15` in the `DataLoader` to improve performance.\n",
      "/Users/birm560/opt/anaconda3/envs/neuromancer9/lib/python3.10/site-packages/lightning/pytorch/loops/fit_loop.py:298: The number of training batches (1) is smaller than the logging interval Trainer(log_every_n_steps=50). Set a lower value for log_every_n_steps if you want to see logs for the training epoch.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0:   0%|          | 0/1 [00:00<?, ?it/s] "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/src/neuromancer/constraint.py:169: UserWarning: Using a target size (torch.Size([])) that is different to the input size (torch.Size([99, 1024])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n",
      "  loss = F.l1_loss(left, right)\n",
      "/Users/birm560/opt/anaconda3/envs/neuromancer9/lib/python3.10/site-packages/lightning/pytorch/utilities/data.py:77: Trying to infer the `batch_size` from an ambiguous collection. The batch size we found is 100. To avoid any miscalculations, use `self.log(..., batch_size=batch_size)`.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0: 100%|██████████| 1/1 [00:11<00:00,  0.09it/s, v_num=8, train_loss_step=2.49e+6, train_loss_epoch=2.49e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 0, global step 1: 'train_loss' reached 2491397.25000 (best 2491397.25000), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=0-step=1.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1:   0%|          | 0/1 [00:00<?, ?it/s, v_num=8, train_loss_step=2.49e+6, train_loss_epoch=2.49e+6]        "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/src/neuromancer/constraint.py:169: UserWarning: Using a target size (torch.Size([])) that is different to the input size (torch.Size([99, 1024])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n",
      "  loss = F.l1_loss(left, right)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1: 100%|██████████| 1/1 [00:04<00:00,  0.23it/s, v_num=8, train_loss_step=2.2e+6, train_loss_epoch=2.2e+6]  "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 1, global step 2: 'train_loss' reached 2197733.75000 (best 2197733.75000), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=1-step=2.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2: 100%|██████████| 1/1 [00:04<00:00,  0.23it/s, v_num=8, train_loss_step=2.11e+6, train_loss_epoch=2.11e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 2, global step 3: 'train_loss' reached 2106942.25000 (best 2106942.25000), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=2-step=3.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 3: 100%|██████████| 1/1 [00:04<00:00,  0.22it/s, v_num=8, train_loss_step=2.04e+6, train_loss_epoch=2.04e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 3, global step 4: 'train_loss' reached 2037659.62500 (best 2037659.62500), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=3-step=4.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 4: 100%|██████████| 1/1 [00:04<00:00,  0.23it/s, v_num=8, train_loss_step=1.94e+6, train_loss_epoch=1.94e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 4, global step 5: 'train_loss' reached 1942911.87500 (best 1942911.87500), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=4-step=5.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 5: 100%|██████████| 1/1 [00:04<00:00,  0.23it/s, v_num=8, train_loss_step=1.9e+6, train_loss_epoch=1.9e+6]  "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 5, global step 6: 'train_loss' reached 1903310.25000 (best 1903310.25000), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=5-step=6.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 6: 100%|██████████| 1/1 [00:04<00:00,  0.20it/s, v_num=8, train_loss_step=1.86e+6, train_loss_epoch=1.86e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 6, global step 7: 'train_loss' reached 1855765.87500 (best 1855765.87500), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=6-step=7.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 7: 100%|██████████| 1/1 [00:05<00:00,  0.20it/s, v_num=8, train_loss_step=1.82e+6, train_loss_epoch=1.82e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 7, global step 8: 'train_loss' reached 1819223.62500 (best 1819223.62500), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=7-step=8.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 8: 100%|██████████| 1/1 [00:04<00:00,  0.21it/s, v_num=8, train_loss_step=1.75e+6, train_loss_epoch=1.75e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 8, global step 9: 'train_loss' reached 1752186.12500 (best 1752186.12500), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=8-step=9.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 9: 100%|██████████| 1/1 [00:04<00:00,  0.21it/s, v_num=8, train_loss_step=1.73e+6, train_loss_epoch=1.73e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 9, global step 10: 'train_loss' reached 1726925.75000 (best 1726925.75000), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=9-step=10.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 10: 100%|██████████| 1/1 [00:06<00:00,  0.17it/s, v_num=8, train_loss_step=1.65e+6, train_loss_epoch=1.65e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 10, global step 11: 'train_loss' reached 1650899.50000 (best 1650899.50000), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=10-step=11.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 11: 100%|██████████| 1/1 [00:05<00:00,  0.17it/s, v_num=8, train_loss_step=1.65e+6, train_loss_epoch=1.65e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 11, global step 12: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 12: 100%|██████████| 1/1 [00:06<00:00,  0.16it/s, v_num=8, train_loss_step=1.59e+6, train_loss_epoch=1.59e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 12, global step 13: 'train_loss' reached 1594603.37500 (best 1594603.37500), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=12-step=13.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 13: 100%|██████████| 1/1 [00:04<00:00,  0.21it/s, v_num=8, train_loss_step=1.63e+6, train_loss_epoch=1.63e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 13, global step 14: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 14: 100%|██████████| 1/1 [00:03<00:00,  0.25it/s, v_num=8, train_loss_step=1.57e+6, train_loss_epoch=1.57e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 14, global step 15: 'train_loss' reached 1565642.12500 (best 1565642.12500), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=14-step=15.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 15: 100%|██████████| 1/1 [00:04<00:00,  0.24it/s, v_num=8, train_loss_step=1.61e+6, train_loss_epoch=1.61e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 15, global step 16: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 16: 100%|██████████| 1/1 [00:04<00:00,  0.21it/s, v_num=8, train_loss_step=1.54e+6, train_loss_epoch=1.54e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 16, global step 17: 'train_loss' reached 1542609.62500 (best 1542609.62500), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=16-step=17.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 17: 100%|██████████| 1/1 [00:04<00:00,  0.24it/s, v_num=8, train_loss_step=1.6e+6, train_loss_epoch=1.6e+6]  "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 17, global step 18: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 18: 100%|██████████| 1/1 [00:04<00:00,  0.23it/s, v_num=8, train_loss_step=1.53e+6, train_loss_epoch=1.53e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 18, global step 19: 'train_loss' reached 1525671.62500 (best 1525671.62500), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=18-step=19.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 19: 100%|██████████| 1/1 [00:04<00:00,  0.22it/s, v_num=8, train_loss_step=1.59e+6, train_loss_epoch=1.59e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 19, global step 20: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 20: 100%|██████████| 1/1 [00:04<00:00,  0.24it/s, v_num=8, train_loss_step=1.51e+6, train_loss_epoch=1.51e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 20, global step 21: 'train_loss' reached 1510765.87500 (best 1510765.87500), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=20-step=21.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 21: 100%|██████████| 1/1 [00:04<00:00,  0.24it/s, v_num=8, train_loss_step=1.56e+6, train_loss_epoch=1.56e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 21, global step 22: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 22: 100%|██████████| 1/1 [00:03<00:00,  0.25it/s, v_num=8, train_loss_step=1.49e+6, train_loss_epoch=1.49e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 22, global step 23: 'train_loss' reached 1486163.87500 (best 1486163.87500), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=22-step=23.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0:   0%|          | 0/1 [07:11<?, ?it/s], v_num=8, train_loss_step=1.49e+6, train_loss_epoch=1.49e+6]        \n",
      "Epoch 23: 100%|██████████| 1/1 [00:06<00:00,  0.16it/s, v_num=8, train_loss_step=1.53e+6, train_loss_epoch=1.53e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 23, global step 24: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 24: 100%|██████████| 1/1 [00:04<00:00,  0.21it/s, v_num=8, train_loss_step=1.47e+6, train_loss_epoch=1.47e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 24, global step 25: 'train_loss' reached 1465629.75000 (best 1465629.75000), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=24-step=25.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 25: 100%|██████████| 1/1 [00:04<00:00,  0.20it/s, v_num=8, train_loss_step=1.49e+6, train_loss_epoch=1.49e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 25, global step 26: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 26: 100%|██████████| 1/1 [00:05<00:00,  0.19it/s, v_num=8, train_loss_step=1.44e+6, train_loss_epoch=1.44e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 26, global step 27: 'train_loss' reached 1438116.75000 (best 1438116.75000), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=26-step=27.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 27: 100%|██████████| 1/1 [00:05<00:00,  0.19it/s, v_num=8, train_loss_step=1.45e+6, train_loss_epoch=1.45e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 27, global step 28: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 28: 100%|██████████| 1/1 [00:04<00:00,  0.22it/s, v_num=8, train_loss_step=1.41e+6, train_loss_epoch=1.41e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 28, global step 29: 'train_loss' reached 1409233.87500 (best 1409233.87500), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=28-step=29.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 29: 100%|██████████| 1/1 [00:05<00:00,  0.19it/s, v_num=8, train_loss_step=1.41e+6, train_loss_epoch=1.41e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 29, global step 30: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 30: 100%|██████████| 1/1 [00:04<00:00,  0.20it/s, v_num=8, train_loss_step=1.38e+6, train_loss_epoch=1.38e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 30, global step 31: 'train_loss' reached 1379124.50000 (best 1379124.50000), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=30-step=31.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 31: 100%|██████████| 1/1 [00:05<00:00,  0.20it/s, v_num=8, train_loss_step=1.38e+6, train_loss_epoch=1.38e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 31, global step 32: 'train_loss' reached 1375999.87500 (best 1375999.87500), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=31-step=32.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 32: 100%|██████████| 1/1 [00:04<00:00,  0.20it/s, v_num=8, train_loss_step=1.34e+6, train_loss_epoch=1.34e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 32, global step 33: 'train_loss' reached 1344039.25000 (best 1344039.25000), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=32-step=33.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 33: 100%|██████████| 1/1 [00:04<00:00,  0.20it/s, v_num=8, train_loss_step=1.33e+6, train_loss_epoch=1.33e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 33, global step 34: 'train_loss' reached 1333839.25000 (best 1333839.25000), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=33-step=34.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 34: 100%|██████████| 1/1 [00:05<00:00,  0.19it/s, v_num=8, train_loss_step=1.31e+6, train_loss_epoch=1.31e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 34, global step 35: 'train_loss' reached 1314711.62500 (best 1314711.62500), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=34-step=35.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 35: 100%|██████████| 1/1 [00:05<00:00,  0.19it/s, v_num=8, train_loss_step=1.3e+6, train_loss_epoch=1.3e+6]  "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 35, global step 36: 'train_loss' reached 1301437.87500 (best 1301437.87500), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=35-step=36.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 36: 100%|██████████| 1/1 [00:04<00:00,  0.21it/s, v_num=8, train_loss_step=1.29e+6, train_loss_epoch=1.29e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 36, global step 37: 'train_loss' reached 1287377.25000 (best 1287377.25000), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=36-step=37.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 37: 100%|██████████| 1/1 [00:04<00:00,  0.20it/s, v_num=8, train_loss_step=1.27e+6, train_loss_epoch=1.27e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 37, global step 38: 'train_loss' reached 1265593.50000 (best 1265593.50000), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=37-step=38.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 38: 100%|██████████| 1/1 [00:05<00:00,  0.19it/s, v_num=8, train_loss_step=1.25e+6, train_loss_epoch=1.25e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 38, global step 39: 'train_loss' reached 1252697.62500 (best 1252697.62500), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=38-step=39.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 39: 100%|██████████| 1/1 [00:05<00:00,  0.20it/s, v_num=8, train_loss_step=1.23e+6, train_loss_epoch=1.23e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 39, global step 40: 'train_loss' reached 1232948.37500 (best 1232948.37500), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=39-step=40.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 40: 100%|██████████| 1/1 [00:04<00:00,  0.21it/s, v_num=8, train_loss_step=1.23e+6, train_loss_epoch=1.23e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 40, global step 41: 'train_loss' reached 1228424.62500 (best 1228424.62500), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=40-step=41.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 41: 100%|██████████| 1/1 [00:05<00:00,  0.20it/s, v_num=8, train_loss_step=1.21e+6, train_loss_epoch=1.21e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 41, global step 42: 'train_loss' reached 1208733.75000 (best 1208733.75000), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=41-step=42.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 42: 100%|██████████| 1/1 [00:05<00:00,  0.19it/s, v_num=8, train_loss_step=1.22e+6, train_loss_epoch=1.22e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 42, global step 43: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 43: 100%|██████████| 1/1 [00:05<00:00,  0.19it/s, v_num=8, train_loss_step=1.21e+6, train_loss_epoch=1.21e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 43, global step 44: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 44: 100%|██████████| 1/1 [00:06<00:00,  0.17it/s, v_num=8, train_loss_step=1.22e+6, train_loss_epoch=1.22e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 44, global step 45: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 45: 100%|██████████| 1/1 [00:05<00:00,  0.17it/s, v_num=8, train_loss_step=1.17e+6, train_loss_epoch=1.17e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 45, global step 46: 'train_loss' reached 1167783.37500 (best 1167783.37500), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=45-step=46.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 46: 100%|██████████| 1/1 [00:05<00:00,  0.19it/s, v_num=8, train_loss_step=1.18e+6, train_loss_epoch=1.18e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 46, global step 47: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 47: 100%|██████████| 1/1 [00:05<00:00,  0.19it/s, v_num=8, train_loss_step=1.16e+6, train_loss_epoch=1.16e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 47, global step 48: 'train_loss' reached 1156787.37500 (best 1156787.37500), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=47-step=48.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 48: 100%|██████████| 1/1 [00:06<00:00,  0.15it/s, v_num=8, train_loss_step=1.19e+6, train_loss_epoch=1.19e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 48, global step 49: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 49: 100%|██████████| 1/1 [00:05<00:00,  0.17it/s, v_num=8, train_loss_step=1.21e+6, train_loss_epoch=1.21e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 49, global step 50: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 50: 100%|██████████| 1/1 [00:04<00:00,  0.20it/s, v_num=8, train_loss_step=1.26e+6, train_loss_epoch=1.26e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 50, global step 51: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 51:   0%|          | 0/1 [00:00<?, ?it/s, v_num=8, train_loss_step=1.26e+6, train_loss_epoch=1.26e+6]        "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/src/neuromancer/constraint.py:169: UserWarning: Using a target size (torch.Size([])) that is different to the input size (torch.Size([99, 1024])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n",
      "  loss = F.l1_loss(left, right)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 51: 100%|██████████| 1/1 [00:04<00:00,  0.20it/s, v_num=8, train_loss_step=1.15e+6, train_loss_epoch=1.15e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 51, global step 52: 'train_loss' reached 1153392.87500 (best 1153392.87500), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=51-step=52.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 52: 100%|██████████| 1/1 [00:04<00:00,  0.21it/s, v_num=8, train_loss_step=1.18e+6, train_loss_epoch=1.18e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 52, global step 53: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 53: 100%|██████████| 1/1 [00:04<00:00,  0.22it/s, v_num=8, train_loss_step=1.13e+6, train_loss_epoch=1.13e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 53, global step 54: 'train_loss' reached 1130628.00000 (best 1130628.00000), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=53-step=54.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 54: 100%|██████████| 1/1 [00:05<00:00,  0.19it/s, v_num=8, train_loss_step=1.17e+6, train_loss_epoch=1.17e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 54, global step 55: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 55: 100%|██████████| 1/1 [00:05<00:00,  0.18it/s, v_num=8, train_loss_step=1.12e+6, train_loss_epoch=1.12e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 55, global step 56: 'train_loss' reached 1122421.00000 (best 1122421.00000), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=55-step=56.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 56: 100%|██████████| 1/1 [00:05<00:00,  0.19it/s, v_num=8, train_loss_step=1.16e+6, train_loss_epoch=1.16e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 56, global step 57: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 57: 100%|██████████| 1/1 [00:04<00:00,  0.20it/s, v_num=8, train_loss_step=1.12e+6, train_loss_epoch=1.12e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 57, global step 58: 'train_loss' reached 1118608.12500 (best 1118608.12500), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=57-step=58.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 58: 100%|██████████| 1/1 [00:06<00:00,  0.16it/s, v_num=8, train_loss_step=1.16e+6, train_loss_epoch=1.16e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 58, global step 59: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 59: 100%|██████████| 1/1 [00:05<00:00,  0.19it/s, v_num=8, train_loss_step=1.12e+6, train_loss_epoch=1.12e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 59, global step 60: 'train_loss' reached 1116679.25000 (best 1116679.25000), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=59-step=60.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 60: 100%|██████████| 1/1 [00:05<00:00,  0.18it/s, v_num=8, train_loss_step=1.17e+6, train_loss_epoch=1.17e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 60, global step 61: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 61: 100%|██████████| 1/1 [00:05<00:00,  0.18it/s, v_num=8, train_loss_step=1.12e+6, train_loss_epoch=1.12e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 61, global step 62: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 62: 100%|██████████| 1/1 [00:05<00:00,  0.19it/s, v_num=8, train_loss_step=1.17e+6, train_loss_epoch=1.17e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 62, global step 63: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 63: 100%|██████████| 1/1 [00:05<00:00,  0.18it/s, v_num=8, train_loss_step=1.1e+6, train_loss_epoch=1.1e+6]  "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 63, global step 64: 'train_loss' reached 1100457.75000 (best 1100457.75000), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=63-step=64.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 64: 100%|██████████| 1/1 [00:05<00:00,  0.19it/s, v_num=8, train_loss_step=1.15e+6, train_loss_epoch=1.15e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 64, global step 65: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 65: 100%|██████████| 1/1 [00:05<00:00,  0.17it/s, v_num=8, train_loss_step=1.09e+6, train_loss_epoch=1.09e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 65, global step 66: 'train_loss' reached 1086657.12500 (best 1086657.12500), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=65-step=66.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 66: 100%|██████████| 1/1 [00:05<00:00,  0.17it/s, v_num=8, train_loss_step=1.15e+6, train_loss_epoch=1.15e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 66, global step 67: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 67: 100%|██████████| 1/1 [00:05<00:00,  0.19it/s, v_num=8, train_loss_step=1.08e+6, train_loss_epoch=1.08e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 67, global step 68: 'train_loss' reached 1082844.50000 (best 1082844.50000), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=67-step=68.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 68: 100%|██████████| 1/1 [00:05<00:00,  0.19it/s, v_num=8, train_loss_step=1.2e+6, train_loss_epoch=1.2e+6]  "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 68, global step 69: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 69: 100%|██████████| 1/1 [00:05<00:00,  0.19it/s, v_num=8, train_loss_step=1.12e+6, train_loss_epoch=1.12e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 69, global step 70: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 70: 100%|██████████| 1/1 [00:04<00:00,  0.21it/s, v_num=8, train_loss_step=1.25e+6, train_loss_epoch=1.25e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 70, global step 71: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 71: 100%|██████████| 1/1 [00:04<00:00,  0.21it/s, v_num=8, train_loss_step=1.08e+6, train_loss_epoch=1.08e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 71, global step 72: 'train_loss' reached 1082627.12500 (best 1082627.12500), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=71-step=72.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 72: 100%|██████████| 1/1 [00:05<00:00,  0.19it/s, v_num=8, train_loss_step=1.16e+6, train_loss_epoch=1.16e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 72, global step 73: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 73: 100%|██████████| 1/1 [00:05<00:00,  0.19it/s, v_num=8, train_loss_step=1.07e+6, train_loss_epoch=1.07e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 73, global step 74: 'train_loss' reached 1074187.50000 (best 1074187.50000), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=73-step=74.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 74: 100%|██████████| 1/1 [00:05<00:00,  0.18it/s, v_num=8, train_loss_step=1.14e+6, train_loss_epoch=1.14e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 74, global step 75: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 75: 100%|██████████| 1/1 [00:04<00:00,  0.21it/s, v_num=8, train_loss_step=1.06e+6, train_loss_epoch=1.06e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 75, global step 76: 'train_loss' reached 1064947.50000 (best 1064947.50000), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=75-step=76.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 76: 100%|██████████| 1/1 [00:05<00:00,  0.19it/s, v_num=8, train_loss_step=1.12e+6, train_loss_epoch=1.12e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 76, global step 77: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 77: 100%|██████████| 1/1 [00:04<00:00,  0.21it/s, v_num=8, train_loss_step=1.06e+6, train_loss_epoch=1.06e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 77, global step 78: 'train_loss' reached 1056460.50000 (best 1056460.50000), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=77-step=78.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 78: 100%|██████████| 1/1 [00:04<00:00,  0.21it/s, v_num=8, train_loss_step=1.12e+6, train_loss_epoch=1.12e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 78, global step 79: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 79: 100%|██████████| 1/1 [00:04<00:00,  0.20it/s, v_num=8, train_loss_step=1.05e+6, train_loss_epoch=1.05e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 79, global step 80: 'train_loss' reached 1048624.00000 (best 1048624.00000), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=79-step=80.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 80: 100%|██████████| 1/1 [00:05<00:00,  0.19it/s, v_num=8, train_loss_step=1.11e+6, train_loss_epoch=1.11e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 80, global step 81: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 81: 100%|██████████| 1/1 [00:04<00:00,  0.20it/s, v_num=8, train_loss_step=1.04e+6, train_loss_epoch=1.04e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 81, global step 82: 'train_loss' reached 1042008.25000 (best 1042008.25000), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=81-step=82.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 82: 100%|██████████| 1/1 [00:05<00:00,  0.18it/s, v_num=8, train_loss_step=1.1e+6, train_loss_epoch=1.1e+6]  "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 82, global step 83: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 83: 100%|██████████| 1/1 [00:04<00:00,  0.20it/s, v_num=8, train_loss_step=1.04e+6, train_loss_epoch=1.04e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 83, global step 84: 'train_loss' reached 1035015.75000 (best 1035015.75000), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=83-step=84.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 84: 100%|██████████| 1/1 [00:05<00:00,  0.18it/s, v_num=8, train_loss_step=1.11e+6, train_loss_epoch=1.11e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 84, global step 85: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 85: 100%|██████████| 1/1 [00:05<00:00,  0.18it/s, v_num=8, train_loss_step=1.03e+6, train_loss_epoch=1.03e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 85, global step 86: 'train_loss' reached 1033595.81250 (best 1033595.81250), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=85-step=86.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 86: 100%|██████████| 1/1 [00:05<00:00,  0.18it/s, v_num=8, train_loss_step=1.15e+6, train_loss_epoch=1.15e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 86, global step 87: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 87: 100%|██████████| 1/1 [00:04<00:00,  0.21it/s, v_num=8, train_loss_step=1.05e+6, train_loss_epoch=1.05e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 87, global step 88: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 88: 100%|██████████| 1/1 [00:05<00:00,  0.18it/s, v_num=8, train_loss_step=1.27e+6, train_loss_epoch=1.27e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 88, global step 89: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 89: 100%|██████████| 1/1 [00:04<00:00,  0.20it/s, v_num=8, train_loss_step=1.03e+6, train_loss_epoch=1.03e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 89, global step 90: 'train_loss' reached 1031956.75000 (best 1031956.75000), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=89-step=90.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 90: 100%|██████████| 1/1 [00:05<00:00,  0.18it/s, v_num=8, train_loss_step=1.15e+6, train_loss_epoch=1.15e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 90, global step 91: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 91: 100%|██████████| 1/1 [00:05<00:00,  0.19it/s, v_num=8, train_loss_step=1.02e+6, train_loss_epoch=1.02e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 91, global step 92: 'train_loss' reached 1015912.87500 (best 1015912.87500), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=91-step=92.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 92: 100%|██████████| 1/1 [00:05<00:00,  0.18it/s, v_num=8, train_loss_step=1.11e+6, train_loss_epoch=1.11e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 92, global step 93: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 93: 100%|██████████| 1/1 [00:05<00:00,  0.19it/s, v_num=8, train_loss_step=1e+6, train_loss_epoch=1e+6]      "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 93, global step 94: 'train_loss' reached 1001834.18750 (best 1001834.18750), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=93-step=94.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 94: 100%|██████████| 1/1 [00:05<00:00,  0.17it/s, v_num=8, train_loss_step=1.09e+6, train_loss_epoch=1.09e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 94, global step 95: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 95: 100%|██████████| 1/1 [00:05<00:00,  0.18it/s, v_num=8, train_loss_step=1e+6, train_loss_epoch=1e+6]      "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 95, global step 96: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 96: 100%|██████████| 1/1 [00:05<00:00,  0.19it/s, v_num=8, train_loss_step=1.07e+6, train_loss_epoch=1.07e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 96, global step 97: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 97: 100%|██████████| 1/1 [00:05<00:00,  0.19it/s, v_num=8, train_loss_step=9.98e+5, train_loss_epoch=9.98e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 97, global step 98: 'train_loss' reached 997792.81250 (best 997792.81250), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=97-step=98.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 98: 100%|██████████| 1/1 [00:05<00:00,  0.20it/s, v_num=8, train_loss_step=1.06e+6, train_loss_epoch=1.06e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 98, global step 99: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 99: 100%|██████████| 1/1 [00:05<00:00,  0.20it/s, v_num=8, train_loss_step=9.94e+5, train_loss_epoch=9.94e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 99, global step 100: 'train_loss' reached 993626.50000 (best 993626.50000), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=99-step=100.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 100: 100%|██████████| 1/1 [00:05<00:00,  0.18it/s, v_num=8, train_loss_step=1.06e+6, train_loss_epoch=1.06e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 100, global step 101: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 101:   0%|          | 0/1 [00:00<?, ?it/s, v_num=8, train_loss_step=1.06e+6, train_loss_epoch=1.06e+6]        "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/src/neuromancer/constraint.py:169: UserWarning: Using a target size (torch.Size([])) that is different to the input size (torch.Size([99, 1024])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n",
      "  loss = F.l1_loss(left, right)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 101: 100%|██████████| 1/1 [00:04<00:00,  0.24it/s, v_num=8, train_loss_step=9.9e+5, train_loss_epoch=9.9e+5]  "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 101, global step 102: 'train_loss' reached 989654.81250 (best 989654.81250), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=101-step=102.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 102: 100%|██████████| 1/1 [00:04<00:00,  0.23it/s, v_num=8, train_loss_step=1.05e+6, train_loss_epoch=1.05e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 102, global step 103: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 103: 100%|██████████| 1/1 [00:04<00:00,  0.24it/s, v_num=8, train_loss_step=9.85e+5, train_loss_epoch=9.85e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 103, global step 104: 'train_loss' reached 985464.00000 (best 985464.00000), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=103-step=104.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 104: 100%|██████████| 1/1 [00:05<00:00,  0.17it/s, v_num=8, train_loss_step=1.04e+6, train_loss_epoch=1.04e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 104, global step 105: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 105: 100%|██████████| 1/1 [00:05<00:00,  0.19it/s, v_num=8, train_loss_step=9.82e+5, train_loss_epoch=9.82e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 105, global step 106: 'train_loss' reached 982027.81250 (best 982027.81250), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=105-step=106.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 106: 100%|██████████| 1/1 [00:04<00:00,  0.21it/s, v_num=8, train_loss_step=1.03e+6, train_loss_epoch=1.03e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 106, global step 107: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 107: 100%|██████████| 1/1 [00:04<00:00,  0.22it/s, v_num=8, train_loss_step=9.78e+5, train_loss_epoch=9.78e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 107, global step 108: 'train_loss' reached 978099.25000 (best 978099.25000), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=107-step=108.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 108: 100%|██████████| 1/1 [00:05<00:00,  0.20it/s, v_num=8, train_loss_step=1.02e+6, train_loss_epoch=1.02e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 108, global step 109: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 109: 100%|██████████| 1/1 [00:04<00:00,  0.23it/s, v_num=8, train_loss_step=9.77e+5, train_loss_epoch=9.77e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 109, global step 110: 'train_loss' reached 977235.06250 (best 977235.06250), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=109-step=110.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 110: 100%|██████████| 1/1 [00:05<00:00,  0.18it/s, v_num=8, train_loss_step=1.03e+6, train_loss_epoch=1.03e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 110, global step 111: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 111: 100%|██████████| 1/1 [00:04<00:00,  0.20it/s, v_num=8, train_loss_step=9.84e+5, train_loss_epoch=9.84e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 111, global step 112: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 112: 100%|██████████| 1/1 [00:05<00:00,  0.18it/s, v_num=8, train_loss_step=1.04e+6, train_loss_epoch=1.04e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 112, global step 113: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 113: 100%|██████████| 1/1 [00:05<00:00,  0.19it/s, v_num=8, train_loss_step=9.75e+5, train_loss_epoch=9.75e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 113, global step 114: 'train_loss' reached 975105.43750 (best 975105.43750), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=113-step=114.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 114: 100%|██████████| 1/1 [00:05<00:00,  0.19it/s, v_num=8, train_loss_step=1e+6, train_loss_epoch=1e+6]      "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 114, global step 115: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 115: 100%|██████████| 1/1 [00:04<00:00,  0.21it/s, v_num=8, train_loss_step=9.64e+5, train_loss_epoch=9.64e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 115, global step 116: 'train_loss' reached 964200.62500 (best 964200.62500), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=115-step=116.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 116: 100%|██████████| 1/1 [00:05<00:00,  0.20it/s, v_num=8, train_loss_step=9.89e+5, train_loss_epoch=9.89e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 116, global step 117: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 117: 100%|██████████| 1/1 [00:04<00:00,  0.20it/s, v_num=8, train_loss_step=9.62e+5, train_loss_epoch=9.62e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 117, global step 118: 'train_loss' reached 961931.06250 (best 961931.06250), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=117-step=118.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 118: 100%|██████████| 1/1 [00:05<00:00,  0.19it/s, v_num=8, train_loss_step=9.92e+5, train_loss_epoch=9.92e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 118, global step 119: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 119: 100%|██████████| 1/1 [00:05<00:00,  0.20it/s, v_num=8, train_loss_step=9.68e+5, train_loss_epoch=9.68e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 119, global step 120: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 120: 100%|██████████| 1/1 [00:05<00:00,  0.18it/s, v_num=8, train_loss_step=1e+6, train_loss_epoch=1e+6]      "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 120, global step 121: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 121: 100%|██████████| 1/1 [00:05<00:00,  0.19it/s, v_num=8, train_loss_step=9.59e+5, train_loss_epoch=9.59e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 121, global step 122: 'train_loss' reached 959130.18750 (best 959130.18750), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=121-step=122.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 122: 100%|██████████| 1/1 [00:05<00:00,  0.18it/s, v_num=8, train_loss_step=9.91e+5, train_loss_epoch=9.91e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 122, global step 123: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 123: 100%|██████████| 1/1 [00:04<00:00,  0.21it/s, v_num=8, train_loss_step=9.52e+5, train_loss_epoch=9.52e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 123, global step 124: 'train_loss' reached 952406.37500 (best 952406.37500), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=123-step=124.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 124: 100%|██████████| 1/1 [00:05<00:00,  0.18it/s, v_num=8, train_loss_step=1.02e+6, train_loss_epoch=1.02e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 124, global step 125: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 125: 100%|██████████| 1/1 [00:04<00:00,  0.20it/s, v_num=8, train_loss_step=9.72e+5, train_loss_epoch=9.72e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 125, global step 126: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 126: 100%|██████████| 1/1 [00:04<00:00,  0.21it/s, v_num=8, train_loss_step=1.03e+6, train_loss_epoch=1.03e+6]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 126, global step 127: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 127: 100%|██████████| 1/1 [00:05<00:00,  0.19it/s, v_num=8, train_loss_step=9.31e+5, train_loss_epoch=9.31e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 127, global step 128: 'train_loss' reached 931372.18750 (best 931372.18750), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=127-step=128.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 128: 100%|██████████| 1/1 [00:05<00:00,  0.18it/s, v_num=8, train_loss_step=9.67e+5, train_loss_epoch=9.67e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 128, global step 129: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 129: 100%|██████████| 1/1 [00:05<00:00,  0.19it/s, v_num=8, train_loss_step=9.22e+5, train_loss_epoch=9.22e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 129, global step 130: 'train_loss' reached 922280.56250 (best 922280.56250), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=129-step=130.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 130: 100%|██████████| 1/1 [00:05<00:00,  0.20it/s, v_num=8, train_loss_step=9.53e+5, train_loss_epoch=9.53e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 130, global step 131: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 131: 100%|██████████| 1/1 [00:05<00:00,  0.19it/s, v_num=8, train_loss_step=9.17e+5, train_loss_epoch=9.17e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 131, global step 132: 'train_loss' reached 916677.75000 (best 916677.75000), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=131-step=132.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 132: 100%|██████████| 1/1 [00:05<00:00,  0.18it/s, v_num=8, train_loss_step=9.45e+5, train_loss_epoch=9.45e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 132, global step 133: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 133: 100%|██████████| 1/1 [00:05<00:00,  0.19it/s, v_num=8, train_loss_step=9.12e+5, train_loss_epoch=9.12e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 133, global step 134: 'train_loss' reached 912430.75000 (best 912430.75000), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=133-step=134.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 134: 100%|██████████| 1/1 [00:05<00:00,  0.17it/s, v_num=8, train_loss_step=9.4e+5, train_loss_epoch=9.4e+5]  "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 134, global step 135: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 135: 100%|██████████| 1/1 [00:04<00:00,  0.20it/s, v_num=8, train_loss_step=9.1e+5, train_loss_epoch=9.1e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 135, global step 136: 'train_loss' reached 909834.75000 (best 909834.75000), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=135-step=136.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 136: 100%|██████████| 1/1 [00:05<00:00,  0.18it/s, v_num=8, train_loss_step=9.38e+5, train_loss_epoch=9.38e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 136, global step 137: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 137: 100%|██████████| 1/1 [00:05<00:00,  0.19it/s, v_num=8, train_loss_step=9.09e+5, train_loss_epoch=9.09e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 137, global step 138: 'train_loss' reached 909191.25000 (best 909191.25000), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=137-step=138.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 138: 100%|██████████| 1/1 [00:05<00:00,  0.17it/s, v_num=8, train_loss_step=9.39e+5, train_loss_epoch=9.39e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 138, global step 139: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 139: 100%|██████████| 1/1 [00:05<00:00,  0.18it/s, v_num=8, train_loss_step=9.08e+5, train_loss_epoch=9.08e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 139, global step 140: 'train_loss' reached 908384.87500 (best 908384.87500), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=139-step=140.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 140: 100%|██████████| 1/1 [00:05<00:00,  0.18it/s, v_num=8, train_loss_step=9.39e+5, train_loss_epoch=9.39e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 140, global step 141: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 141: 100%|██████████| 1/1 [00:05<00:00,  0.19it/s, v_num=8, train_loss_step=9.05e+5, train_loss_epoch=9.05e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 141, global step 142: 'train_loss' reached 904820.31250 (best 904820.31250), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=141-step=142.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 142: 100%|██████████| 1/1 [00:05<00:00,  0.18it/s, v_num=8, train_loss_step=9.49e+5, train_loss_epoch=9.49e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 142, global step 143: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 143: 100%|██████████| 1/1 [00:06<00:00,  0.17it/s, v_num=8, train_loss_step=8.99e+5, train_loss_epoch=8.99e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 143, global step 144: 'train_loss' reached 899466.18750 (best 899466.18750), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=143-step=144.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 144: 100%|██████████| 1/1 [00:05<00:00,  0.17it/s, v_num=8, train_loss_step=9.72e+5, train_loss_epoch=9.72e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 144, global step 145: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 145: 100%|██████████| 1/1 [00:04<00:00,  0.21it/s, v_num=8, train_loss_step=9.07e+5, train_loss_epoch=9.07e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 145, global step 146: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 146: 100%|██████████| 1/1 [00:04<00:00,  0.21it/s, v_num=8, train_loss_step=9.4e+5, train_loss_epoch=9.4e+5]  "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 146, global step 147: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 147: 100%|██████████| 1/1 [00:04<00:00,  0.22it/s, v_num=8, train_loss_step=8.88e+5, train_loss_epoch=8.88e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 147, global step 148: 'train_loss' reached 887675.37500 (best 887675.37500), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=147-step=148.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 148: 100%|██████████| 1/1 [00:05<00:00,  0.19it/s, v_num=8, train_loss_step=9.16e+5, train_loss_epoch=9.16e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 148, global step 149: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 149: 100%|██████████| 1/1 [00:04<00:00,  0.21it/s, v_num=8, train_loss_step=8.76e+5, train_loss_epoch=8.76e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 149, global step 150: 'train_loss' reached 876120.00000 (best 876120.00000), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=149-step=150.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 150: 100%|██████████| 1/1 [00:05<00:00,  0.19it/s, v_num=8, train_loss_step=9e+5, train_loss_epoch=9e+5]      "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 150, global step 151: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 151:   0%|          | 0/1 [00:00<?, ?it/s, v_num=8, train_loss_step=9e+5, train_loss_epoch=9e+5]        "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/src/neuromancer/constraint.py:169: UserWarning: Using a target size (torch.Size([])) that is different to the input size (torch.Size([99, 1024])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n",
      "  loss = F.l1_loss(left, right)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 151: 100%|██████████| 1/1 [00:04<00:00,  0.24it/s, v_num=8, train_loss_step=8.71e+5, train_loss_epoch=8.71e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 151, global step 152: 'train_loss' reached 870604.50000 (best 870604.50000), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=151-step=152.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 152: 100%|██████████| 1/1 [00:05<00:00,  0.17it/s, v_num=8, train_loss_step=8.94e+5, train_loss_epoch=8.94e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 152, global step 153: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 153: 100%|██████████| 1/1 [00:06<00:00,  0.16it/s, v_num=8, train_loss_step=8.76e+5, train_loss_epoch=8.76e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 153, global step 154: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 154: 100%|██████████| 1/1 [00:05<00:00,  0.17it/s, v_num=8, train_loss_step=9.08e+5, train_loss_epoch=9.08e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 154, global step 155: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 155: 100%|██████████| 1/1 [00:05<00:00,  0.20it/s, v_num=8, train_loss_step=8.84e+5, train_loss_epoch=8.84e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 155, global step 156: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 156: 100%|██████████| 1/1 [00:05<00:00,  0.20it/s, v_num=8, train_loss_step=9.1e+5, train_loss_epoch=9.1e+5]  "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 156, global step 157: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 157: 100%|██████████| 1/1 [00:04<00:00,  0.22it/s, v_num=8, train_loss_step=8.75e+5, train_loss_epoch=8.75e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 157, global step 158: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 158: 100%|██████████| 1/1 [00:04<00:00,  0.21it/s, v_num=8, train_loss_step=8.93e+5, train_loss_epoch=8.93e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 158, global step 159: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 159: 100%|██████████| 1/1 [00:04<00:00,  0.20it/s, v_num=8, train_loss_step=8.64e+5, train_loss_epoch=8.64e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 159, global step 160: 'train_loss' reached 864327.50000 (best 864327.50000), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=159-step=160.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 160: 100%|██████████| 1/1 [00:05<00:00,  0.19it/s, v_num=8, train_loss_step=8.77e+5, train_loss_epoch=8.77e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 160, global step 161: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 161: 100%|██████████| 1/1 [00:05<00:00,  0.18it/s, v_num=8, train_loss_step=8.56e+5, train_loss_epoch=8.56e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 161, global step 162: 'train_loss' reached 856493.50000 (best 856493.50000), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=161-step=162.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 162: 100%|██████████| 1/1 [00:05<00:00,  0.19it/s, v_num=8, train_loss_step=8.64e+5, train_loss_epoch=8.64e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 162, global step 163: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 163: 100%|██████████| 1/1 [00:04<00:00,  0.21it/s, v_num=8, train_loss_step=8.47e+5, train_loss_epoch=8.47e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 163, global step 164: 'train_loss' reached 847342.81250 (best 847342.81250), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=163-step=164.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 164: 100%|██████████| 1/1 [00:05<00:00,  0.18it/s, v_num=8, train_loss_step=8.5e+5, train_loss_epoch=8.5e+5]  "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 164, global step 165: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 165: 100%|██████████| 1/1 [00:05<00:00,  0.18it/s, v_num=8, train_loss_step=8.4e+5, train_loss_epoch=8.4e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 165, global step 166: 'train_loss' reached 839696.75000 (best 839696.75000), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=165-step=166.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 166: 100%|██████████| 1/1 [00:06<00:00,  0.17it/s, v_num=8, train_loss_step=8.44e+5, train_loss_epoch=8.44e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 166, global step 167: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 167: 100%|██████████| 1/1 [00:06<00:00,  0.17it/s, v_num=8, train_loss_step=8.4e+5, train_loss_epoch=8.4e+5]  "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 167, global step 168: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 168: 100%|██████████| 1/1 [00:05<00:00,  0.18it/s, v_num=8, train_loss_step=8.53e+5, train_loss_epoch=8.53e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 168, global step 169: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 169: 100%|██████████| 1/1 [00:05<00:00,  0.17it/s, v_num=8, train_loss_step=8.47e+5, train_loss_epoch=8.47e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 169, global step 170: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 170: 100%|██████████| 1/1 [00:05<00:00,  0.20it/s, v_num=8, train_loss_step=8.71e+5, train_loss_epoch=8.71e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 170, global step 171: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 171: 100%|██████████| 1/1 [00:05<00:00,  0.18it/s, v_num=8, train_loss_step=8.56e+5, train_loss_epoch=8.56e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 171, global step 172: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 172: 100%|██████████| 1/1 [00:05<00:00,  0.19it/s, v_num=8, train_loss_step=8.93e+5, train_loss_epoch=8.93e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 172, global step 173: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 173: 100%|██████████| 1/1 [00:05<00:00,  0.19it/s, v_num=8, train_loss_step=8.52e+5, train_loss_epoch=8.52e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 173, global step 174: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 174: 100%|██████████| 1/1 [00:05<00:00,  0.19it/s, v_num=8, train_loss_step=8.72e+5, train_loss_epoch=8.72e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 174, global step 175: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 175: 100%|██████████| 1/1 [00:05<00:00,  0.18it/s, v_num=8, train_loss_step=8.33e+5, train_loss_epoch=8.33e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 175, global step 176: 'train_loss' reached 833024.87500 (best 833024.87500), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=175-step=176.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 176: 100%|██████████| 1/1 [00:05<00:00,  0.18it/s, v_num=8, train_loss_step=8.44e+5, train_loss_epoch=8.44e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 176, global step 177: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 177: 100%|██████████| 1/1 [00:04<00:00,  0.20it/s, v_num=8, train_loss_step=8.13e+5, train_loss_epoch=8.13e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 177, global step 178: 'train_loss' reached 813011.37500 (best 813011.37500), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=177-step=178.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 178: 100%|██████████| 1/1 [00:05<00:00,  0.20it/s, v_num=8, train_loss_step=8.18e+5, train_loss_epoch=8.18e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 178, global step 179: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 179: 100%|██████████| 1/1 [00:04<00:00,  0.20it/s, v_num=8, train_loss_step=8.03e+5, train_loss_epoch=8.03e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 179, global step 180: 'train_loss' reached 803010.75000 (best 803010.75000), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=179-step=180.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 180: 100%|██████████| 1/1 [00:05<00:00,  0.20it/s, v_num=8, train_loss_step=8.12e+5, train_loss_epoch=8.12e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 180, global step 181: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 181: 100%|██████████| 1/1 [00:04<00:00,  0.21it/s, v_num=8, train_loss_step=8.01e+5, train_loss_epoch=8.01e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 181, global step 182: 'train_loss' reached 800698.81250 (best 800698.81250), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=181-step=182.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 182: 100%|██████████| 1/1 [00:04<00:00,  0.20it/s, v_num=8, train_loss_step=8.15e+5, train_loss_epoch=8.15e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 182, global step 183: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 183: 100%|██████████| 1/1 [00:05<00:00,  0.19it/s, v_num=8, train_loss_step=8.03e+5, train_loss_epoch=8.03e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 183, global step 184: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 184: 100%|██████████| 1/1 [00:05<00:00,  0.20it/s, v_num=8, train_loss_step=8.17e+5, train_loss_epoch=8.17e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 184, global step 185: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 185: 100%|██████████| 1/1 [00:04<00:00,  0.20it/s, v_num=8, train_loss_step=7.93e+5, train_loss_epoch=7.93e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 185, global step 186: 'train_loss' reached 793109.81250 (best 793109.81250), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=185-step=186.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 186: 100%|██████████| 1/1 [00:05<00:00,  0.19it/s, v_num=8, train_loss_step=8.01e+5, train_loss_epoch=8.01e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 186, global step 187: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 187: 100%|██████████| 1/1 [00:04<00:00,  0.22it/s, v_num=8, train_loss_step=7.83e+5, train_loss_epoch=7.83e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 187, global step 188: 'train_loss' reached 782935.43750 (best 782935.43750), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=187-step=188.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 188: 100%|██████████| 1/1 [00:05<00:00,  0.19it/s, v_num=8, train_loss_step=7.94e+5, train_loss_epoch=7.94e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 188, global step 189: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 189: 100%|██████████| 1/1 [00:04<00:00,  0.21it/s, v_num=8, train_loss_step=7.81e+5, train_loss_epoch=7.81e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 189, global step 190: 'train_loss' reached 781267.68750 (best 781267.68750), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=189-step=190.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 190: 100%|██████████| 1/1 [00:04<00:00,  0.21it/s, v_num=8, train_loss_step=8.07e+5, train_loss_epoch=8.07e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 190, global step 191: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 191: 100%|██████████| 1/1 [00:05<00:00,  0.20it/s, v_num=8, train_loss_step=7.91e+5, train_loss_epoch=7.91e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 191, global step 192: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 192: 100%|██████████| 1/1 [00:04<00:00,  0.21it/s, v_num=8, train_loss_step=8.27e+5, train_loss_epoch=8.27e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 192, global step 193: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 193: 100%|██████████| 1/1 [00:04<00:00,  0.21it/s, v_num=8, train_loss_step=7.8e+5, train_loss_epoch=7.8e+5]  "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 193, global step 194: 'train_loss' reached 780222.25000 (best 780222.25000), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=193-step=194.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 194: 100%|██████████| 1/1 [00:05<00:00,  0.19it/s, v_num=8, train_loss_step=8.2e+5, train_loss_epoch=8.2e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 194, global step 195: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 195: 100%|██████████| 1/1 [00:05<00:00,  0.18it/s, v_num=8, train_loss_step=7.65e+5, train_loss_epoch=7.65e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 195, global step 196: 'train_loss' reached 764846.87500 (best 764846.87500), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=195-step=196.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 196: 100%|██████████| 1/1 [00:05<00:00,  0.18it/s, v_num=8, train_loss_step=8.04e+5, train_loss_epoch=8.04e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 196, global step 197: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 197: 100%|██████████| 1/1 [00:05<00:00,  0.19it/s, v_num=8, train_loss_step=7.51e+5, train_loss_epoch=7.51e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 197, global step 198: 'train_loss' reached 750746.75000 (best 750746.75000), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=197-step=198.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 198: 100%|██████████| 1/1 [00:05<00:00,  0.19it/s, v_num=8, train_loss_step=7.85e+5, train_loss_epoch=7.85e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 198, global step 199: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 199: 100%|██████████| 1/1 [00:05<00:00,  0.19it/s, v_num=8, train_loss_step=7.38e+5, train_loss_epoch=7.38e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 199, global step 200: 'train_loss' reached 738258.75000 (best 738258.75000), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=199-step=200.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 200: 100%|██████████| 1/1 [00:05<00:00,  0.19it/s, v_num=8, train_loss_step=7.68e+5, train_loss_epoch=7.68e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 200, global step 201: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 201:   0%|          | 0/1 [00:00<?, ?it/s, v_num=8, train_loss_step=7.68e+5, train_loss_epoch=7.68e+5]        "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/src/neuromancer/constraint.py:169: UserWarning: Using a target size (torch.Size([])) that is different to the input size (torch.Size([99, 1024])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n",
      "  loss = F.l1_loss(left, right)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 201: 100%|██████████| 1/1 [00:05<00:00,  0.18it/s, v_num=8, train_loss_step=7.32e+5, train_loss_epoch=7.32e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 201, global step 202: 'train_loss' reached 731544.00000 (best 731544.00000), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=201-step=202.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 202: 100%|██████████| 1/1 [00:05<00:00,  0.19it/s, v_num=8, train_loss_step=7.61e+5, train_loss_epoch=7.61e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 202, global step 203: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 203: 100%|██████████| 1/1 [00:05<00:00,  0.20it/s, v_num=8, train_loss_step=7.26e+5, train_loss_epoch=7.26e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 203, global step 204: 'train_loss' reached 726355.25000 (best 726355.25000), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=203-step=204.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 204: 100%|██████████| 1/1 [00:05<00:00,  0.18it/s, v_num=8, train_loss_step=7.52e+5, train_loss_epoch=7.52e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 204, global step 205: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 205: 100%|██████████| 1/1 [00:05<00:00,  0.20it/s, v_num=8, train_loss_step=7.21e+5, train_loss_epoch=7.21e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 205, global step 206: 'train_loss' reached 721088.56250 (best 721088.56250), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=205-step=206.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 206: 100%|██████████| 1/1 [00:05<00:00,  0.17it/s, v_num=8, train_loss_step=7.45e+5, train_loss_epoch=7.45e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 206, global step 207: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 207: 100%|██████████| 1/1 [00:05<00:00,  0.19it/s, v_num=8, train_loss_step=7.28e+5, train_loss_epoch=7.28e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 207, global step 208: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 208: 100%|██████████| 1/1 [00:05<00:00,  0.18it/s, v_num=8, train_loss_step=7.89e+5, train_loss_epoch=7.89e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 208, global step 209: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 209: 100%|██████████| 1/1 [00:06<00:00,  0.15it/s, v_num=8, train_loss_step=7.58e+5, train_loss_epoch=7.58e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 209, global step 210: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 210: 100%|██████████| 1/1 [00:06<00:00,  0.15it/s, v_num=8, train_loss_step=7.73e+5, train_loss_epoch=7.73e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 210, global step 211: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 211: 100%|██████████| 1/1 [00:06<00:00,  0.17it/s, v_num=8, train_loss_step=7.43e+5, train_loss_epoch=7.43e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 211, global step 212: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 212: 100%|██████████| 1/1 [00:05<00:00,  0.18it/s, v_num=8, train_loss_step=7.39e+5, train_loss_epoch=7.39e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 212, global step 213: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 213: 100%|██████████| 1/1 [00:05<00:00,  0.18it/s, v_num=8, train_loss_step=7.34e+5, train_loss_epoch=7.34e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 213, global step 214: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 214: 100%|██████████| 1/1 [00:05<00:00,  0.19it/s, v_num=8, train_loss_step=7.17e+5, train_loss_epoch=7.17e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 214, global step 215: 'train_loss' reached 717460.75000 (best 717460.75000), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=214-step=215.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 215: 100%|██████████| 1/1 [00:05<00:00,  0.18it/s, v_num=8, train_loss_step=7.05e+5, train_loss_epoch=7.05e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 215, global step 216: 'train_loss' reached 705393.81250 (best 705393.81250), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=215-step=216.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 216: 100%|██████████| 1/1 [00:05<00:00,  0.18it/s, v_num=8, train_loss_step=6.95e+5, train_loss_epoch=6.95e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 216, global step 217: 'train_loss' reached 694682.50000 (best 694682.50000), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=216-step=217.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 217: 100%|██████████| 1/1 [00:05<00:00,  0.18it/s, v_num=8, train_loss_step=6.86e+5, train_loss_epoch=6.86e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 217, global step 218: 'train_loss' reached 685694.56250 (best 685694.56250), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=217-step=218.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 218: 100%|██████████| 1/1 [00:05<00:00,  0.19it/s, v_num=8, train_loss_step=6.84e+5, train_loss_epoch=6.84e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 218, global step 219: 'train_loss' reached 683777.81250 (best 683777.81250), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=218-step=219.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 219: 100%|██████████| 1/1 [00:06<00:00,  0.16it/s, v_num=8, train_loss_step=6.75e+5, train_loss_epoch=6.75e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 219, global step 220: 'train_loss' reached 675055.37500 (best 675055.37500), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=219-step=220.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 220: 100%|██████████| 1/1 [00:05<00:00,  0.18it/s, v_num=8, train_loss_step=6.77e+5, train_loss_epoch=6.77e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 220, global step 221: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 221: 100%|██████████| 1/1 [00:05<00:00,  0.18it/s, v_num=8, train_loss_step=6.67e+5, train_loss_epoch=6.67e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 221, global step 222: 'train_loss' reached 666944.62500 (best 666944.62500), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=221-step=222.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 222: 100%|██████████| 1/1 [00:05<00:00,  0.18it/s, v_num=8, train_loss_step=6.74e+5, train_loss_epoch=6.74e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 222, global step 223: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 223: 100%|██████████| 1/1 [00:05<00:00,  0.17it/s, v_num=8, train_loss_step=6.65e+5, train_loss_epoch=6.65e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 223, global step 224: 'train_loss' reached 664711.06250 (best 664711.06250), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=223-step=224.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 224: 100%|██████████| 1/1 [00:05<00:00,  0.18it/s, v_num=8, train_loss_step=6.82e+5, train_loss_epoch=6.82e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 224, global step 225: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 225: 100%|██████████| 1/1 [00:05<00:00,  0.19it/s, v_num=8, train_loss_step=6.74e+5, train_loss_epoch=6.74e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 225, global step 226: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 226: 100%|██████████| 1/1 [00:05<00:00,  0.18it/s, v_num=8, train_loss_step=6.89e+5, train_loss_epoch=6.89e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 226, global step 227: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 227: 100%|██████████| 1/1 [00:04<00:00,  0.20it/s, v_num=8, train_loss_step=6.8e+5, train_loss_epoch=6.8e+5]  "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 227, global step 228: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 228: 100%|██████████| 1/1 [00:05<00:00,  0.20it/s, v_num=8, train_loss_step=6.78e+5, train_loss_epoch=6.78e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 228, global step 229: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 229: 100%|██████████| 1/1 [00:05<00:00,  0.20it/s, v_num=8, train_loss_step=6.81e+5, train_loss_epoch=6.81e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 229, global step 230: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 230: 100%|██████████| 1/1 [00:04<00:00,  0.21it/s, v_num=8, train_loss_step=6.63e+5, train_loss_epoch=6.63e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 230, global step 231: 'train_loss' reached 662526.93750 (best 662526.93750), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=230-step=231.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 231: 100%|██████████| 1/1 [00:05<00:00,  0.18it/s, v_num=8, train_loss_step=6.8e+5, train_loss_epoch=6.8e+5]  "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 231, global step 232: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 232: 100%|██████████| 1/1 [00:04<00:00,  0.20it/s, v_num=8, train_loss_step=6.45e+5, train_loss_epoch=6.45e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 232, global step 233: 'train_loss' reached 644878.12500 (best 644878.12500), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=232-step=233.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 233: 100%|██████████| 1/1 [00:05<00:00,  0.19it/s, v_num=8, train_loss_step=6.7e+5, train_loss_epoch=6.7e+5]  "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 233, global step 234: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 234: 100%|██████████| 1/1 [00:04<00:00,  0.21it/s, v_num=8, train_loss_step=6.32e+5, train_loss_epoch=6.32e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 234, global step 235: 'train_loss' reached 631917.56250 (best 631917.56250), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=234-step=235.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 235: 100%|██████████| 1/1 [00:05<00:00,  0.19it/s, v_num=8, train_loss_step=6.58e+5, train_loss_epoch=6.58e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 235, global step 236: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 236: 100%|██████████| 1/1 [00:04<00:00,  0.20it/s, v_num=8, train_loss_step=6.23e+5, train_loss_epoch=6.23e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 236, global step 237: 'train_loss' reached 623494.62500 (best 623494.62500), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=236-step=237.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 237: 100%|██████████| 1/1 [00:05<00:00,  0.20it/s, v_num=8, train_loss_step=6.42e+5, train_loss_epoch=6.42e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 237, global step 238: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 238: 100%|██████████| 1/1 [00:04<00:00,  0.21it/s, v_num=8, train_loss_step=6e+5, train_loss_epoch=6e+5]      "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 238, global step 239: 'train_loss' reached 599682.00000 (best 599682.00000), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=238-step=239.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 239: 100%|██████████| 1/1 [00:05<00:00,  0.20it/s, v_num=8, train_loss_step=6.16e+5, train_loss_epoch=6.16e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 239, global step 240: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 240: 100%|██████████| 1/1 [00:05<00:00,  0.20it/s, v_num=8, train_loss_step=5.87e+5, train_loss_epoch=5.87e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 240, global step 241: 'train_loss' reached 586777.56250 (best 586777.56250), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=240-step=241.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 241: 100%|██████████| 1/1 [00:05<00:00,  0.18it/s, v_num=8, train_loss_step=6.02e+5, train_loss_epoch=6.02e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 241, global step 242: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 242: 100%|██████████| 1/1 [00:05<00:00,  0.18it/s, v_num=8, train_loss_step=5.76e+5, train_loss_epoch=5.76e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 242, global step 243: 'train_loss' reached 576311.93750 (best 576311.93750), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=242-step=243.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 243: 100%|██████████| 1/1 [00:05<00:00,  0.19it/s, v_num=8, train_loss_step=5.91e+5, train_loss_epoch=5.91e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 243, global step 244: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 244: 100%|██████████| 1/1 [00:05<00:00,  0.17it/s, v_num=8, train_loss_step=5.67e+5, train_loss_epoch=5.67e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 244, global step 245: 'train_loss' reached 566722.68750 (best 566722.68750), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=244-step=245.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 245: 100%|██████████| 1/1 [00:06<00:00,  0.17it/s, v_num=8, train_loss_step=5.81e+5, train_loss_epoch=5.81e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 245, global step 246: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 246: 100%|██████████| 1/1 [00:05<00:00,  0.17it/s, v_num=8, train_loss_step=5.57e+5, train_loss_epoch=5.57e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 246, global step 247: 'train_loss' reached 557291.93750 (best 557291.93750), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=246-step=247.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 247: 100%|██████████| 1/1 [00:06<00:00,  0.17it/s, v_num=8, train_loss_step=5.74e+5, train_loss_epoch=5.74e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 247, global step 248: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 248: 100%|██████████| 1/1 [00:05<00:00,  0.18it/s, v_num=8, train_loss_step=5.54e+5, train_loss_epoch=5.54e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 248, global step 249: 'train_loss' reached 553969.93750 (best 553969.93750), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=248-step=249.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 249: 100%|██████████| 1/1 [00:06<00:00,  0.16it/s, v_num=8, train_loss_step=5.83e+5, train_loss_epoch=5.83e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 249, global step 250: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 250: 100%|██████████| 1/1 [00:05<00:00,  0.17it/s, v_num=8, train_loss_step=5.8e+5, train_loss_epoch=5.8e+5]  "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 250, global step 251: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 251:   0%|          | 0/1 [00:00<?, ?it/s, v_num=8, train_loss_step=5.8e+5, train_loss_epoch=5.8e+5]        "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/src/neuromancer/constraint.py:169: UserWarning: Using a target size (torch.Size([])) that is different to the input size (torch.Size([99, 1024])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n",
      "  loss = F.l1_loss(left, right)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 251: 100%|██████████| 1/1 [00:06<00:00,  0.16it/s, v_num=8, train_loss_step=6.22e+5, train_loss_epoch=6.22e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 251, global step 252: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 252: 100%|██████████| 1/1 [00:05<00:00,  0.17it/s, v_num=8, train_loss_step=6.07e+5, train_loss_epoch=6.07e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 252, global step 253: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 253: 100%|██████████| 1/1 [00:05<00:00,  0.17it/s, v_num=8, train_loss_step=6.35e+5, train_loss_epoch=6.35e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 253, global step 254: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 254: 100%|██████████| 1/1 [00:05<00:00,  0.18it/s, v_num=8, train_loss_step=5.66e+5, train_loss_epoch=5.66e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 254, global step 255: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 255: 100%|██████████| 1/1 [00:05<00:00,  0.17it/s, v_num=8, train_loss_step=5.93e+5, train_loss_epoch=5.93e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 255, global step 256: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 256: 100%|██████████| 1/1 [00:05<00:00,  0.17it/s, v_num=8, train_loss_step=5.39e+5, train_loss_epoch=5.39e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 256, global step 257: 'train_loss' reached 538557.50000 (best 538557.50000), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=256-step=257.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 257: 100%|██████████| 1/1 [00:07<00:00,  0.14it/s, v_num=8, train_loss_step=5.56e+5, train_loss_epoch=5.56e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 257, global step 258: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 258: 100%|██████████| 1/1 [00:06<00:00,  0.16it/s, v_num=8, train_loss_step=5.21e+5, train_loss_epoch=5.21e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 258, global step 259: 'train_loss' reached 520979.15625 (best 520979.15625), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=258-step=259.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 259: 100%|██████████| 1/1 [00:07<00:00,  0.14it/s, v_num=8, train_loss_step=5.36e+5, train_loss_epoch=5.36e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 259, global step 260: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 260: 100%|██████████| 1/1 [00:07<00:00,  0.14it/s, v_num=8, train_loss_step=5.16e+5, train_loss_epoch=5.16e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 260, global step 261: 'train_loss' reached 516467.28125 (best 516467.28125), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=260-step=261.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 261: 100%|██████████| 1/1 [00:07<00:00,  0.13it/s, v_num=8, train_loss_step=5.3e+5, train_loss_epoch=5.3e+5]  "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 261, global step 262: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 262: 100%|██████████| 1/1 [00:07<00:00,  0.14it/s, v_num=8, train_loss_step=5.14e+5, train_loss_epoch=5.14e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 262, global step 263: 'train_loss' reached 513872.81250 (best 513872.81250), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=262-step=263.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 263: 100%|██████████| 1/1 [00:07<00:00,  0.14it/s, v_num=8, train_loss_step=5.25e+5, train_loss_epoch=5.25e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 263, global step 264: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 264: 100%|██████████| 1/1 [00:06<00:00,  0.15it/s, v_num=8, train_loss_step=5.23e+5, train_loss_epoch=5.23e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 264, global step 265: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 265: 100%|██████████| 1/1 [00:06<00:00,  0.14it/s, v_num=8, train_loss_step=5.59e+5, train_loss_epoch=5.59e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 265, global step 266: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 266: 100%|██████████| 1/1 [00:07<00:00,  0.14it/s, v_num=8, train_loss_step=5.31e+5, train_loss_epoch=5.31e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 266, global step 267: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 267: 100%|██████████| 1/1 [00:06<00:00,  0.14it/s, v_num=8, train_loss_step=5.05e+5, train_loss_epoch=5.05e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 267, global step 268: 'train_loss' reached 505196.81250 (best 505196.81250), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=267-step=268.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 268: 100%|██████████| 1/1 [00:06<00:00,  0.15it/s, v_num=8, train_loss_step=5.15e+5, train_loss_epoch=5.15e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 268, global step 269: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 269: 100%|██████████| 1/1 [00:06<00:00,  0.15it/s, v_num=8, train_loss_step=5.01e+5, train_loss_epoch=5.01e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 269, global step 270: 'train_loss' reached 501311.43750 (best 501311.43750), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=269-step=270.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 270: 100%|██████████| 1/1 [00:06<00:00,  0.15it/s, v_num=8, train_loss_step=5.12e+5, train_loss_epoch=5.12e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 270, global step 271: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 271: 100%|██████████| 1/1 [00:06<00:00,  0.15it/s, v_num=8, train_loss_step=5.04e+5, train_loss_epoch=5.04e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 271, global step 272: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 272: 100%|██████████| 1/1 [00:05<00:00,  0.17it/s, v_num=8, train_loss_step=5.14e+5, train_loss_epoch=5.14e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 272, global step 273: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 273: 100%|██████████| 1/1 [00:06<00:00,  0.16it/s, v_num=8, train_loss_step=5.13e+5, train_loss_epoch=5.13e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 273, global step 274: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 274: 100%|██████████| 1/1 [00:06<00:00,  0.16it/s, v_num=8, train_loss_step=5.17e+5, train_loss_epoch=5.17e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 274, global step 275: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 275: 100%|██████████| 1/1 [00:06<00:00,  0.16it/s, v_num=8, train_loss_step=5.26e+5, train_loss_epoch=5.26e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 275, global step 276: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 276: 100%|██████████| 1/1 [00:06<00:00,  0.15it/s, v_num=8, train_loss_step=5.15e+5, train_loss_epoch=5.15e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 276, global step 277: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 277: 100%|██████████| 1/1 [00:07<00:00,  0.14it/s, v_num=8, train_loss_step=5.23e+5, train_loss_epoch=5.23e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 277, global step 278: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 278: 100%|██████████| 1/1 [00:06<00:00,  0.17it/s, v_num=8, train_loss_step=4.78e+5, train_loss_epoch=4.78e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 278, global step 279: 'train_loss' reached 477984.96875 (best 477984.96875), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=278-step=279.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 279: 100%|██████████| 1/1 [00:06<00:00,  0.15it/s, v_num=8, train_loss_step=4.76e+5, train_loss_epoch=4.76e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 279, global step 280: 'train_loss' reached 475755.90625 (best 475755.90625), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=279-step=280.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 280: 100%|██████████| 1/1 [00:06<00:00,  0.15it/s, v_num=8, train_loss_step=4.59e+5, train_loss_epoch=4.59e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 280, global step 281: 'train_loss' reached 459466.84375 (best 459466.84375), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=280-step=281.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 281: 100%|██████████| 1/1 [00:06<00:00,  0.15it/s, v_num=8, train_loss_step=4.61e+5, train_loss_epoch=4.61e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 281, global step 282: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 282: 100%|██████████| 1/1 [00:05<00:00,  0.17it/s, v_num=8, train_loss_step=4.53e+5, train_loss_epoch=4.53e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 282, global step 283: 'train_loss' reached 452608.56250 (best 452608.56250), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=282-step=283.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 283: 100%|██████████| 1/1 [00:06<00:00,  0.16it/s, v_num=8, train_loss_step=4.56e+5, train_loss_epoch=4.56e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 283, global step 284: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 284: 100%|██████████| 1/1 [00:06<00:00,  0.15it/s, v_num=8, train_loss_step=4.5e+5, train_loss_epoch=4.5e+5]  "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 284, global step 285: 'train_loss' reached 450411.43750 (best 450411.43750), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=284-step=285.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 285: 100%|██████████| 1/1 [00:06<00:00,  0.16it/s, v_num=8, train_loss_step=4.62e+5, train_loss_epoch=4.62e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 285, global step 286: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 286: 100%|██████████| 1/1 [00:05<00:00,  0.17it/s, v_num=8, train_loss_step=4.51e+5, train_loss_epoch=4.51e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 286, global step 287: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 287: 100%|██████████| 1/1 [00:06<00:00,  0.16it/s, v_num=8, train_loss_step=4.72e+5, train_loss_epoch=4.72e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 287, global step 288: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 288: 100%|██████████| 1/1 [00:06<00:00,  0.16it/s, v_num=8, train_loss_step=4.53e+5, train_loss_epoch=4.53e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 288, global step 289: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 289: 100%|██████████| 1/1 [00:06<00:00,  0.15it/s, v_num=8, train_loss_step=4.85e+5, train_loss_epoch=4.85e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 289, global step 290: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 290: 100%|██████████| 1/1 [00:06<00:00,  0.15it/s, v_num=8, train_loss_step=4.6e+5, train_loss_epoch=4.6e+5]  "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 290, global step 291: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 291: 100%|██████████| 1/1 [00:05<00:00,  0.17it/s, v_num=8, train_loss_step=4.94e+5, train_loss_epoch=4.94e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 291, global step 292: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 292: 100%|██████████| 1/1 [00:06<00:00,  0.16it/s, v_num=8, train_loss_step=4.66e+5, train_loss_epoch=4.66e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 292, global step 293: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 293: 100%|██████████| 1/1 [00:06<00:00,  0.16it/s, v_num=8, train_loss_step=4.8e+5, train_loss_epoch=4.8e+5]  "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 293, global step 294: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 294: 100%|██████████| 1/1 [00:06<00:00,  0.16it/s, v_num=8, train_loss_step=4.68e+5, train_loss_epoch=4.68e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 294, global step 295: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 295: 100%|██████████| 1/1 [00:06<00:00,  0.15it/s, v_num=8, train_loss_step=4.61e+5, train_loss_epoch=4.61e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 295, global step 296: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 296: 100%|██████████| 1/1 [00:06<00:00,  0.16it/s, v_num=8, train_loss_step=4.68e+5, train_loss_epoch=4.68e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 296, global step 297: 'train_loss' was not in top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 297: 100%|██████████| 1/1 [00:05<00:00,  0.18it/s, v_num=8, train_loss_step=4.42e+5, train_loss_epoch=4.42e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 297, global step 298: 'train_loss' reached 441544.46875 (best 441544.46875), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=297-step=298.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 298: 100%|██████████| 1/1 [00:06<00:00,  0.15it/s, v_num=8, train_loss_step=4.4e+5, train_loss_epoch=4.4e+5]  "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 298, global step 299: 'train_loss' reached 439501.28125 (best 439501.28125), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=298-step=299.ckpt' as top 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 299: 100%|██████████| 1/1 [00:06<00:00,  0.17it/s, v_num=8, train_loss_step=4.17e+5, train_loss_epoch=4.17e+5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 299, global step 300: 'train_loss' reached 417461.09375 (best 417461.09375), saving model to '/Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs/epoch=299-step=300.ckpt' as top 1\n",
      "`Trainer.fit` stopped: `max_epochs=300` reached.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 299: 100%|██████████| 1/1 [00:06<00:00,  0.16it/s, v_num=8, train_loss_step=4.17e+5, train_loss_epoch=4.17e+5]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Fix the same Brownian motion for visualization.\n",
    "bm_vis = torchsde.BrownianInterval(\n",
    "    t0=t0, t1=t1, size=(batch_size, latent_size,), device='cpu', levy_area_approximation=\"space-time\")\n",
    "\n",
    "# Define the custom_training_step to support visualization. \n",
    "def custom_training_step(model, batch): \n",
    "    output = model.problem(batch)\n",
    "    loss = output[model.train_metric]\n",
    "    img_path = os.path.join('', f'current_epoch_{model.current_epoch:06d}.pdf')\n",
    "    if model.current_epoch % 50 == 0: \n",
    "        vis(batch,  model.problem, bm_vis, img_path, num_samples=10)\n",
    "    return loss\n",
    "\n",
    "\n",
    "optimizer = torch.optim.Adam(problem.parameters(), lr=0.001)\n",
    "lit_trainer = LitTrainer(epochs=300, accelerator='cpu', train_metric='train_loss', \n",
    "                         dev_metric='train_loss', eval_metric='train_loss', test_metric='train_loss',\n",
    "                         custom_optimizer=optimizer, custom_training_step=custom_training_step)\n",
    "\n",
    "\n",
    "\n",
    "lit_trainer.fit(problem=problem, data_setup_function=make_dataset,  t0=t0, t1=t1, batch_size=batch_size, noise_std=noise_std, steps=steps)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Classes found in this walkthrough can be found in [sde.py](../../src/neuromancer/dynamics/sde.py)*"
   ]
  }
 ],
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  "kernelspec": {
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    "name": "ipython",
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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